U.S. patent number 11,133,076 [Application Number 16/547,401] was granted by the patent office on 2021-09-28 for efficient relocation of data between storage devices of a storage system.
This patent grant is currently assigned to PURE STORAGE, INC.. The grantee listed for this patent is Pure Storage, Inc.. Invention is credited to Andrew R. Bernat, Ronald Karr, Wei Tang.
United States Patent |
11,133,076 |
Karr , et al. |
September 28, 2021 |
Efficient relocation of data between storage devices of a storage
system
Abstract
A storage system includes a plurality of solid-state storage
devices and a storage controller operatively coupled to the
plurality of solid-state storage devices, the storage controller
including a processing device, the processing device to receive,
from a first solid-state storage device of the plurality of
solid-state storage devices, an indication that data stored at the
first solid-state storage device is affected by read disturb. In
response to receiving the indication, the processing device is
further to identify a second solid-state storage device of the
plurality of solid-state storage devices for the relocation of the
data and transmit, to the first solid-state storage device, a
command comprising information associated with the relocation of
the data from the first solid-state storage device to the second
solid-state storage device, wherein the command causes the first
solid-state storage device to relocate the data to the second
solid-state storage device.
Inventors: |
Karr; Ronald (Palo Alto,
CA), Bernat; Andrew R. (Mountain View, CA), Tang; Wei
(San Jose, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Pure Storage, Inc. |
Mountain View |
CA |
US |
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Assignee: |
PURE STORAGE, INC. (Mountain
View, CA)
|
Family
ID: |
69719979 |
Appl.
No.: |
16/547,401 |
Filed: |
August 21, 2019 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20200082890 A1 |
Mar 12, 2020 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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16389675 |
Apr 19, 2019 |
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62729597 |
Sep 11, 2018 |
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62727792 |
Sep 6, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
3/067 (20130101); G06F 3/0679 (20130101); G06F
11/076 (20130101); G06F 12/0246 (20130101); G06F
11/1076 (20130101); G06F 3/0617 (20130101); G06F
11/0757 (20130101); G06F 3/0688 (20130101); G06F
3/0647 (20130101); G11C 16/26 (20130101); G06F
3/0619 (20130101); G06F 12/0253 (20130101); G11C
16/3418 (20130101); G06F 2212/7208 (20130101) |
Current International
Class: |
G11C
16/34 (20060101); G06F 12/02 (20060101); G06F
3/06 (20060101); G11C 16/26 (20060101); G06F
11/07 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO-1995/002349 |
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WO |
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WO-1999/013403 |
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WO |
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WO-2008/102347 |
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WO |
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WO-2010/071655 |
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Jun 2010 |
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WO |
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Other References
Microsoft Corporation, "GCSettings.IsServerGC Property", Retrieved
Oct. 27, 2013 via the WayBack Machine, 3 pages. cited by applicant
.
Microsoft Corporation, "Fundamentals of Garbage Collection",
Retrieved Aug. 30, 2013 via the WayBack Machine, 11 pages. cited by
applicant.
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Primary Examiner: Dudek, Jr.; Edward J
Assistant Examiner: Verderamo, III; Ralph A
Attorney, Agent or Firm: Womble Bond Dickinson (US) LLP
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. application Ser.
No. 16/389,675, filed on Apr. 19, 2019, which claims the benefit of
U.S. Provisional Patent Application No. 62/727,792, filed on Sep.
6, 2018 and U.S. Provisional Patent Application No. 62/729,597,
filed on Sep. 11, 2018, which are hereby incorporated by reference
in their entireties.
Claims
What is claimed is:
1. A storage system comprising: a plurality of solid-state storage
devices; and a storage controller operatively coupled and external
to the plurality of solid-state storage devices, the storage
controller comprising a processing device, the processing device
to: receive, from a first solid-state storage device of the
plurality of solid-state storage devices, an indication that data
stored at the first solid-state storage device is affected by read
disturb; identify a second solid-state storage device of the
plurality of solid-state storage devices for relocation of the data
upon receiving the indication; and transmit, to the first
solid-state storage device, a command comprising information
associated with the relocation of the data from the first
solid-state storage device to the second solid-state storage
device, wherein the command causes the first solid-state storage
device to relocate the data to the second solid-state storage
device while bypassing the storage controller.
2. The storage system of claim 1, wherein the information
associated with the relocation of the data comprises one or more
transformation operations to be performed on the data and wherein
the one or more transformation operations comprise encryption of
the data.
3. The storage system of claim 1, wherein the data is transmitted
from the first solid-state storage device to the second solid-state
storage device via a mesh network.
4. The storage system of claim 1, wherein the processing device is
further to: update a mapping data structure to deallocate the data
stored at the first solid-state storage device and associate the
data with the second solid-state storage device upon receiving an
acknowledgment that the data has been relocated from the second
solid-state storage device.
5. The storage system of claim 1, wherein the command comprising
the information associated with the relocation of the data
comprises an identification of the data stored in an encrypted
segment of data, wherein the command causes the first solid-state
storage device to decrypt the encrypted segment and extract the
data from the encrypted segment.
6. The storage system of claim 1, wherein the relocation of the
data from the first solid-state storage device to the second
solid-state storage device is associated with an accelerated
garbage collection operation.
7. The storage system of claim 1, wherein the data being affected
by read disturb comprises a read count associated with the data
satisfying a read count threshold.
8. A method, comprising: receiving, by a processing device of a
storage controller from a first solid-state storage device of a
plurality of solid-state storage devices, an indication that data
stored at the first solid-state storage device is affected by read
disturb, wherein the storage controller is external to the
plurality of solid state storage devices; identifying a second
solid-state storage device of the plurality of solid-state storage
devices for relocation of the data upon receiving the indication;
and transmitting, to the first solid-state storage device, a
command comprising information associated with the relocation of
the data from the first solid-state storage device to the second
solid-state storage device, wherein the command causes the first
solid-state storage device to relocate the data to the second
solid-state storage device while bypassing the storage
controller.
9. The method of claim 8, wherein the information associated with
the relocation of the data comprises one or more transformation
operations to be performed on the data and wherein the one or more
transformation operations comprise a format change of the data.
10. The method of claim 8, wherein the data is transmitted from the
first solid-state storage device to the second solid-state storage
device via a network switch.
11. The method of claim 8, further comprising: updating a mapping
data structure to deallocate the data stored at the first
solid-state storage device and associate the data with the second
solid-state storage device upon receiving an acknowledgment that
the data has been relocated from the second solid-state storage
device.
12. The method of claim 8, wherein the command comprising the
information associated with the relocation of the data comprises an
identification of the data stored in an encrypted segment of data,
wherein the command causes the first solid-state storage device to
decrypt the encrypted segment and extract the data from the
encrypted segment.
13. The method of claim 8, wherein the relocation of the data from
the first solid-state storage device to the second solid-state
storage device is associated with an accelerated garbage collection
operation.
14. The method of claim 8, wherein the data being affected by read
disturb comprises an error rate associated with the data satisfying
an error rate threshold.
15. A non-transitory computer readable storage medium storing
instructions, which when executed, cause a processing device of a
storage controller to: receive, by the processing device from a
first solid-state storage device of a plurality of solid-state
storage devices, an indication that data stored at the first
solid-state storage device is affected by read disturb, wherein the
storage controller is external to the plurality of solid state
storage devices; identify a second solid-state storage device of
the plurality of solid-state storage devices for relocation of the
data upon receiving the indication; and transmit, to the first
solid-state storage device, a command comprising information
associated with the relocation of the data from the first
solid-state storage device to the second solid-state storage
device, wherein the command causes the first solid-state storage
device to relocate the data to the second solid-state storage
device while bypassing the storage controller.
16. The non-transitory computer readable storage medium of claim
15, wherein the information associated with the relocation of the
data comprises one or more transformation operations to be
performed on the data and wherein the one or more transformation
operations comprise a modification to encoded metadata associated
with the data.
17. The non-transitory computer readable storage medium of claim
15, wherein the processing device is further to: update a mapping
data structure to deallocate the data stored at the first
solid-state storage device and associate the data with the second
solid-state storage device upon receiving an acknowledgment that
the data has been relocated from the second solid-state storage
device.
18. The non-transitory computer readable storage medium of claim
15, wherein the command comprising the information associated with
the relocation of the data comprises an identification of the data
stored in an encrypted segment of data, wherein the command causes
the first solid-state storage device to decrypt the encrypted
segment and extract the data from the encrypted segment.
19. The non-transitory computer readable storage medium of claim
15, wherein the relocation of the data from the first solid-state
storage device to the second solid-state storage device is
associated with an accelerated garbage collection operation.
20. The non-transitory computer readable storage medium of claim
15, wherein the data being affected by read disturb comprises an
amount of time associated with the data satisfying a time
threshold.
Description
BACKGROUND
Storage systems, such as enterprise storage systems, may include a
centralized or decentralized repository for data that provides
common data management, data protection, and data sharing
functions, for example, through connections to computer
systems.
BRIEF DESCRIPTION OF DRAWINGS
FIG. 1A illustrates a first example system for data storage in
accordance with some implementations.
FIG. 1B illustrates a second example system for data storage in
accordance with some implementations.
FIG. 1C illustrates a third example system for data storage in
accordance with some implementations.
FIG. 1D illustrates a fourth example system for data storage in
accordance with some implementations.
FIG. 2A is a perspective view of a storage cluster with multiple
storage nodes and internal storage coupled to each storage node to
provide network attached storage, in accordance with some
embodiments.
FIG. 2B is a block diagram showing an interconnect switch coupling
multiple storage nodes in accordance with some embodiments.
FIG. 2C is a multiple level block diagram, showing contents of a
storage node and contents of one of the non-volatile solid state
storage units in accordance with some embodiments.
FIG. 2D shows a storage server environment, which uses embodiments
of the storage nodes and storage units of some previous figures in
accordance with some embodiments.
FIG. 2E is a blade hardware block diagram, showing a control plane,
compute and storage planes, and authorities interacting with
underlying physical resources, in accordance with some
embodiments.
FIG. 2F depicts elasticity software layers in blades of a storage
cluster, in accordance with some embodiments.
FIG. 2G depicts authorities and storage resources in blades of a
storage cluster, in accordance with some embodiments.
FIG. 3A sets forth a diagram of a storage system that is coupled
for data communications with a cloud services provider in
accordance with some embodiments of the present disclosure.
FIG. 3B sets forth a diagram of a storage system in accordance with
some embodiments of the present disclosure.
FIG. 3C sets forth an example of a cloud-based storage system in
accordance with some embodiments of the present disclosure.
FIG. 3D illustrates an exemplary computing device that may be
specifically configured to perform one or more of the processes
described herein.
FIG. 4 is an example method to locally program data stored at
storage device to a new data block in accordance with embodiments
of the disclosure.
FIG. 5 is an example method to locally relocate data stored at
storage device to a new data block in accordance with embodiments
of the disclosure.
FIG. 6A is an illustration of an example of a storage device of a
storage system tracking information associated with data stored at
the storage device in accordance with embodiments of the
disclosure.
FIG. 6B is an illustration of an example of a storage device of a
storage system transmitting an indication to a storage controller
in accordance with embodiments of the disclosure.
FIG. 6C is an illustration of an example of a storage device of a
storage system relocating data from a first data block to a second
data block in accordance with embodiments of the disclosure.
FIG. 7A is an illustration of an example of a storage device of a
storage system storing data at a host system buffer in accordance
with embodiments of the disclosure.
FIG. 7B is an illustration of an example of a storage device of a
storage system receiving data from a host system buffer for storage
at a new data block of the storage device in accordance with
embodiments of the disclosure.
FIG. 8A is an illustration of an example of a storage controller of
a storage system transmitting a command to a first storage device
to relocate data to a second storage device in accordance with
embodiments of the disclosure.
FIG. 8B is an illustration of a first storage device of a storage
system relocating data to a second storage device of the storage
system in accordance with an embodiment of the disclosure.
FIG. 8C is an illustration of a second storage device of storage
system transmitting an acknowledgement to a storage controller in
accordance with some embodiments of the disclosure.
FIG. 9 is an illustration of an example of a storage controller of
storage system transmitting a command to a storage device to
extract data from an encrypted segment in accordance with
embodiments of the disclosure.
FIG. 10 is an illustration of an example of performing a
transformation operation on data being relocated by a storage
system in accordance with embodiments of the disclosure.
FIG. 11 is an example method to relocate data stored at storage
device to a different storage device in accordance with embodiments
of the disclosure.
DETAILED DESCRIPTION
In embodiments, a storage system includes a storage controller that
sends and receives data to/from a host system and manages the data
stored at storage devices of the storage system. Each of the
storage devices includes memory cells that store data. Each of the
memory cells may be programmed in different modes based on desired
characteristics of the memory cell. For example, a memory cell of
the storage device may be programmed in single-level cell (SLC)
mode to store one bit of data per cell or the memory cell may be
programmed in quad-level cell (QLC) mode to store four bits of data
per cell.
Memory cells programmed using programming modes having lower
storage densities, such as SLC mode, may exhibit different
characteristics in comparison to memory cells programmed using
programming modes having higher storage densities, such as QLC
mode. For example, a memory cell programmed in SLC mode may have a
longer data retention time than a memory cell programmed in QLC
mode. Additionally, a memory cell programmed in SLC mode may be
less susceptible to read disturb effecting the data stored at the
memory cell when compared to a memory cell programmed in QLC mode.
Read disturb is the result of frequently reading of data at a
particular memory cell of the storage device causing errors in data
stored at memory cells that are proximate (e.g., located on
adjacent word lines and/or bit lines) to the particular memory
cell. To mitigate the errors in the data as a result of retention
time, read disturb and/or other stresses, the storage system may
relocate the data stored at the storage devices of the storage
system to a new location.
In a conventional storage system, the storage controller may
relocate the data by sending a command to the storage device to
read the data to be relocated and provide the data to the storage
controller. The storage controller may allocate one or more data
blocks of the storage devices of the storage system for storage of
the data to be relocated. The storage controller may then insert
the data to be relocated into a data segment for programming to the
allocated data block(s) of the storage device.
To increase the storage densities of storage systems, more memory
cells of the storage devices are programmed using programming modes
having higher storage densities, such as QLC mode. However, as the
storage densities of the storage devices increase, the frequency
that data stored at the storage devices is relocated to mitigate
errors in the data also increases. For example, a solid-state
storage device having memory cells programmed in QLC mode may
require more relocation operations than a storage device having
memory cells programmed in SLC mode. As a result, the data to be
relocated is transmitted from the storage device to the storage
controller, consuming communication bandwidth between the storage
device and the storage controller. The consumption of communication
bandwidth for the transmission of data associated with relocating
the data may prevent or delay the transmission of other data, such
as user data, from the storage device to the storage controller,
decreasing the performance of the storage system.
Turning now to embodiments of a storage system that utilizes a
storage device of a storage system to locally relocate data stored
at the storage device to a new location at the storage device. The
storage device may identify data that is to be relocated based on
the occurrence of a triggering event. Examples of a triggering
event may be an error rate associated with the data satisfying an
error rate threshold, a read count associated with the data
satisfying a read count threshold and/or an amount of time since
the data was programmed satisfying a time threshold.
The storage device may send an indication to the storage
controller. The indication may include address information
associated with the data and information associated with the
triggering event. For example, the indication may state that data
stored at data block A has an associated error rate that satisfies
an error rate threshold, or has some other characteristic
indicating that action should be taken due to increased likelihood
of an unrecoverable or difficult to recover error in the near
future. In embodiments, other heuristics may be used to identify
data to be relocated based on the occurrence of a triggering event.
In some embodiments, a machine learning algorithm may be
implemented to identify data to be relocated. For example, a
machine learning algorithm may be trained to identify data stored
at a storage device that has been impacted by read disturb and
should be relocated.
In embodiments, upon receiving the indication, the storage
controller may determine whether to have the storage device locally
relocate the data to a new location on the storage device. For
example, if the storage controller determines that the data stored
at the storage device is no longer active or has been de-allocated,
then the storage controller may determine to not have the storage
device locally relocate the data to a new location on the storage
device.
In some embodiments, the storage controller may determine to
initiate an accelerated garbage collection operation. In
embodiments, the data to be relocated may be part of shard of an
erasure coded protected stripe that includes shards stored across
several storage devices of the storage system. The relocation of
the data in the erasure coded protected stripe may trigger the
recalculation of erasure coded protection information for the
erasure coded stripe.
If the storage controller determines to relocate the data to a new
location on the storage device, the storage controller may allocate
a new data block of the storage device for the storage of the data.
Once the new data block has been allocated, the storage controller
may then transmit a command to the storage device. The command may
include address information for the data block currently storing
the data as well as address information for the new data block
allocated by the storage controller to store the relocated data.
The command may cause the storage device to relocate the data from
the data block currently storing the data to the new data block
without sending the data to the storage controller. In some
embodiments, rather than relocating the data to a new data block on
the same storage device, the data may be relocated to a different
storage device of the storage system. For example, the data may be
relocated to a different storage device of the storage system via a
mesh network or a network switch.
Accordingly, embodiments of the disclosure provide for an improved
storage system by having a storage device locally relocate data to
a new data block of the storage device without sending the data to
the storage controller. By having storage device locally relocate
the data, the data is no longer transmitted to the storage
controller, preserving communication bandwidth and improving the
performance of the storage system.
Although embodiments are described as relocating data of a storage
device for the purpose of mitigating errors in the data, aspects of
the disclosure may be used for any purpose requiring the relocation
of data from one data block of a storage device of a storage system
to another data block of the storage system without providing the
data to the storage controller of the storage system.
Example methods, apparatus, and products for the efficient
relocation of data between storage devices of a storage system in
accordance with embodiments of the present disclosure are described
with reference to the accompanying drawings, beginning with FIG.
1A. FIG. 1A illustrates an example system for data storage, in
accordance with some implementations. System 100 (also referred to
as "storage system" herein) includes numerous elements for purposes
of illustration rather than limitation. It may be noted that system
100 may include the same, more, or fewer elements configured in the
same or different manner in other implementations.
System 100 includes a number of computing devices 164A-B. Computing
devices (also referred to as "client devices" herein) may be
embodied, for example, a server in a data center, a workstation, a
personal computer, a notebook, or the like. Computing devices
164A-B may be coupled for data communications to one or more
storage arrays 102A-B through a storage area network (`SAN`) 158 or
a local area network (`LAN`) 160.
The SAN 158 may be implemented with a variety of data
communications fabrics, devices, and protocols. For example, the
fabrics for SAN 158 may include Fibre Channel, Ethernet,
Infiniband, Serial Attached Small Computer System Interface
(`SAS`), or the like. Data communications protocols for use with
SAN 158 may include Advanced Technology Attachment (`ATA`), Fibre
Channel Protocol, Small Computer System Interface (`SCSI`),
Internet Small Computer System Interface (`iSCSI`), HyperSCSI,
Non-Volatile Memory Express (`NVMe`) over Fabrics, or the like. It
may be noted that SAN 158 is provided for illustration, rather than
limitation. Other data communication couplings may be implemented
between computing devices 164A-B and storage arrays 102A-B.
The LAN 160 may also be implemented with a variety of fabrics,
devices, and protocols. For example, the fabrics for LAN 160 may
include Ethernet (802.3), wireless (802.11), or the like. Data
communication protocols for use in LAN 160 may include Transmission
Control Protocol (`TCP`), User Datagram Protocol (`UDP`), Internet
Protocol (`IP`), HyperText Transfer Protocol (`HTTP`), Wireless
Access Protocol (`WAP`), Handheld Device Transport Protocol
(`HDTP`), Session Initiation Protocol (`SIP`), Real Time Protocol
(`RTP`), or the like.
Storage arrays 102A-B may provide persistent data storage for the
computing devices 164A-B. Storage array 102A may be contained in a
chassis (not shown), and storage array 102B may be contained in
another chassis (not shown), in implementations. Storage array 102A
and 102B may include one or more storage array controllers 110A-D
(also referred to as "controller" herein). A storage array
controller 110A-D may be embodied as a module of automated
computing machinery comprising computer hardware, computer
software, or a combination of computer hardware and software. In
some implementations, the storage array controllers 110A-D may be
configured to carry out various storage tasks. Storage tasks may
include writing data received from the computing devices 164A-B to
storage array 102A-B, erasing data from storage array 102A-B,
retrieving data from storage array 102A-B and providing data to
computing devices 164A-B, monitoring and reporting of disk
utilization and performance, performing redundancy operations, such
as Redundant Array of Independent Drives (`RAID`) or RAID-like data
redundancy operations, compressing data, encrypting data, and so
forth.
Storage array controller 110A-D may be implemented in a variety of
ways, including as a Field Programmable Gate Array (`FPGA`), a
Programmable Logic Chip (`PLC`), an Application Specific Integrated
Circuit (`ASIC`), System-on-Chip (`SOC`), or any computing device
that includes discrete components such as a processing device,
central processing unit, computer memory, or various adapters.
Storage array controller 110A-D may include, for example, a data
communications adapter configured to support communications via the
SAN 158 or LAN 160. In some implementations, storage array
controller 110A-D may be independently coupled to the LAN 160. In
implementations, storage array controller 110A-D may include an I/O
controller or the like that couples the storage array controller
110A-D for data communications, through a midplane (not shown), to
a persistent storage resource 170A-B (also referred to as a
"storage resource" herein). The persistent storage resource 170A-B
main include any number of storage drives 171A-F (also referred to
as "storage devices" herein) and any number of non-volatile Random
Access Memory (`NVRAM`) devices (not shown).
In some implementations, the NVRAM devices of a persistent storage
resource 170A-B may be configured to receive, from the storage
array controller 110A-D, data to be stored in the storage drives
171A-F. In some examples, the data may originate from computing
devices 164A-B. In some examples, writing data to the NVRAM device
may be carried out more quickly than directly writing data to the
storage drive 171A-F. In implementations, the storage array
controller 110A-D may be configured to utilize the NVRAM devices as
a quickly accessible buffer for data destined to be written to the
storage drives 171A-F. Latency for write requests using NVRAM
devices as a buffer may be improved relative to a system in which a
storage array controller 110A-D writes data directly to the storage
drives 171A-F. In some implementations, the NVRAM devices may be
implemented with computer memory in the form of high bandwidth, low
latency RAM. The NVRAM device is referred to as "non-volatile"
because the NVRAM device may receive or include a unique power
source that maintains the state of the RAM after main power loss to
the NVRAM device. Such a power source may be a battery, one or more
capacitors, or the like. In response to a power loss, the NVRAM
device may be configured to write the contents of the RAM to a
persistent storage, such as the storage drives 171A-F.
In implementations, storage drive 171A-F may refer to any device
configured to record data persistently, where "persistently" or
"persistent" refers as to a device's ability to maintain recorded
data after loss of power. In some implementations, storage drive
171A-F may correspond to non-disk storage media. For example, the
storage drive 171A-F may be one or more solid-state drives
(`SSDs`), flash memory based storage, any type of solid-state
non-volatile memory, or any other type of non-mechanical storage
device. In other implementations, storage drive 171A-F may include
mechanical or spinning hard disk, such as hard-disk drives
(`HDD`).
In some implementations, the storage array controllers 110A-D may
be configured for offloading device management responsibilities
from storage drive 171A-F in storage array 102A-B. For example,
storage array controllers 110A-D may manage control information
that may describe the state of one or more memory blocks in the
storage drives 171A-F. The control information may indicate, for
example, that a particular memory block has failed and should no
longer be written to, that a particular memory block contains boot
code for a storage array controller 110A-D, the number of
program-erase (`P/E`) cycles that have been performed on a
particular memory block, the age of data stored in a particular
memory block, the type of data that is stored in a particular
memory block, and so forth. In some implementations, the control
information may be stored with an associated memory block as
metadata. In other implementations, the control information for the
storage drives 171A-F may be stored in one or more particular
memory blocks of the storage drives 171A-F that are selected by the
storage array controller 110A-D. The selected memory blocks may be
tagged with an identifier indicating that the selected memory block
contains control information. The identifier may be utilized by the
storage array controllers 110A-D in conjunction with storage drives
171A-F to quickly identify the memory blocks that contain control
information. For example, the storage controllers 110A-D may issue
a command to locate memory blocks that contain control information.
It may be noted that control information may be so large that parts
of the control information may be stored in multiple locations,
that the control information may be stored in multiple locations
for purposes of redundancy, for example, or that the control
information may otherwise be distributed across multiple memory
blocks in the storage drive 171A-F.
In implementations, storage array controllers 110A-D may offload
device management responsibilities from storage drives 171A-F of
storage array 102A-B by retrieving, from the storage drives 171A-F,
control information describing the state of one or more memory
blocks in the storage drives 171A-F. Retrieving the control
information from the storage drives 171A-F may be carried out, for
example, by the storage array controller 110A-D querying the
storage drives 171A-F for the location of control information for a
particular storage drive 171A-F. The storage drives 171A-F may be
configured to execute instructions that enable the storage drive
171A-F to identify the location of the control information. The
instructions may be executed by a controller (not shown) associated
with or otherwise located on the storage drive 171A-F and may cause
the storage drive 171A-F to scan a portion of each memory block to
identify the memory blocks that store control information for the
storage drives 171A-F. The storage drives 171A-F may respond by
sending a response message to the storage array controller 110A-D
that includes the location of control information for the storage
drive 171A-F. Responsive to receiving the response message, storage
array controllers 110A-D may issue a request to read data stored at
the address associated with the location of control information for
the storage drives 171A-F.
In other implementations, the storage array controllers 110A-D may
further offload device management responsibilities from storage
drives 171A-F by performing, in response to receiving the control
information, a storage drive management operation. A storage drive
management operation may include, for example, an operation that is
typically performed by the storage drive 171A-F (e.g., the
controller (not shown) associated with a particular storage drive
171A-F). A storage drive management operation may include, for
example, ensuring that data is not written to failed memory blocks
within the storage drive 171A-F, ensuring that data is written to
memory blocks within the storage drive 171A-F in such a way that
adequate wear leveling is achieved, and so forth.
In implementations, storage array 102A-B may implement two or more
storage array controllers 110A-D. For example, storage array 102A
may include storage array controllers 110A and storage array
controllers 110B. At a given instance, a single storage array
controller 110A-D (e.g., storage array controller 110A) of a
storage system 100 may be designated with primary status (also
referred to as "primary controller" herein), and other storage
array controllers 110A-D (e.g., storage array controller 110A) may
be designated with secondary status (also referred to as "secondary
controller" herein). The primary controller may have particular
rights, such as permission to alter data in persistent storage
resource 170A-B (e.g., writing data to persistent storage resource
170A-B). At least some of the rights of the primary controller may
supersede the rights of the secondary controller. For instance, the
secondary controller may not have permission to alter data in
persistent storage resource 170A-B when the primary controller has
the right. The status of storage array controllers 110A-D may
change. For example, storage array controller 110A may be
designated with secondary status, and storage array controller 110B
may be designated with primary status.
In some implementations, a primary controller, such as storage
array controller 110A, may serve as the primary controller for one
or more storage arrays 102A-B, and a second controller, such as
storage array controller 110B, may serve as the secondary
controller for the one or more storage arrays 102A-B. For example,
storage array controller 110A may be the primary controller for
storage array 102A and storage array 102B, and storage array
controller 110B may be the secondary controller for storage array
102A and 102B. In some implementations, storage array controllers
110C and 110D (also referred to as "storage processing modules")
may neither have primary or secondary status. Storage array
controllers 110C and 110D, implemented as storage processing
modules, may act as a communication interface between the primary
and secondary controllers (e.g., storage array controllers 110A and
110B, respectively) and storage array 102B. For example, storage
array controller 110A of storage array 102A may send a write
request, via SAN 158, to storage array 102B. The write request may
be received by both storage array controllers 110C and 110D of
storage array 102B. Storage array controllers 110C and 110D
facilitate the communication, e.g., send the write request to the
appropriate storage drive 171A-F. It may be noted that in some
implementations storage processing modules may be used to increase
the number of storage drives controlled by the primary and
secondary controllers.
In implementations, storage array controllers 110A-D are
communicatively coupled, via a midplane (not shown), to one or more
storage drives 171A-F and to one or more NVRAM devices (not shown)
that are included as part of a storage array 102A-B. The storage
array controllers 110A-D may be coupled to the midplane via one or
more data communication links and the midplane may be coupled to
the storage drives 171A-F and the NVRAM devices via one or more
data communications links. The data communications links described
herein are collectively illustrated by data communications links
108A-D and may include a Peripheral Component Interconnect Express
(`PCIe`) bus, for example.
FIG. 1B illustrates an example system for data storage, in
accordance with some implementations. Storage array controller 101
illustrated in FIG. 1B may similar to the storage array controllers
110A-D described with respect to FIG. 1A. In one example, storage
array controller 101 may be similar to storage array controller
110A or storage array controller 110B. Storage array controller 101
includes numerous elements for purposes of illustration rather than
limitation. It may be noted that storage array controller 101 may
include the same, more, or fewer elements configured in the same or
different manner in other implementations. It may be noted that
elements of FIG. 1A may be included below to help illustrate
features of storage array controller 101.
Storage array controller 101 may include one or more processing
devices 104 and random access memory (`RAM`) 111. Processing device
104 (or controller 101) represents one or more general-purpose
processing devices such as a microprocessor, central processing
unit, or the like. More particularly, the processing device 104 (or
controller 101) may be a complex instruction set computing (`CISC`)
microprocessor, reduced instruction set computing (`RISC`)
microprocessor, very long instruction word (`VLIW`) microprocessor,
or a processor implementing other instruction sets or processors
implementing a combination of instruction sets. The processing
device 104 (or controller 101) may also be one or more
special-purpose processing devices such as an application specific
integrated circuit (`ASIC`), a field programmable gate array
(`FPGA`), a digital signal processor (`DSP`), network processor, or
the like.
The processing device 104 may be connected to the RAM 111 via a
data communications link 106, which may be embodied as a high speed
memory bus such as a Double-Data Rate 4 (`DDR4`) bus. Stored in RAM
111 is an operating system 112. In some implementations,
instructions 113 are stored in RAM 111. Instructions 113 may
include computer program instructions for performing operations in
in a direct-mapped flash storage system. In one embodiment, a
direct-mapped flash storage system is one that that addresses data
blocks within flash drives directly and without an address
translation performed by the storage controllers of the flash
drives.
In implementations, storage array controller 101 includes one or
more host bus adapters 103A-C that are coupled to the processing
device 104 via a data communications link 105A-C. In
implementations, host bus adapters 103A-C may be computer hardware
that connects a host system (e.g., the storage array controller) to
other network and storage arrays. In some examples, host bus
adapters 103A-C may be a Fibre Channel adapter that enables the
storage array controller 101 to connect to a SAN, an Ethernet
adapter that enables the storage array controller 101 to connect to
a LAN, or the like. Host bus adapters 103A-C may be coupled to the
processing device 104 via a data communications link 105A-C such
as, for example, a PCIe bus.
In implementations, storage array controller 101 may include a host
bus adapter 114 that is coupled to an expander 115. The expander
115 may be used to attach a host system to a larger number of
storage drives. The expander 115 may, for example, be a SAS
expander utilized to enable the host bus adapter 114 to attach to
storage drives in an implementation where the host bus adapter 114
is embodied as a SAS controller.
In implementations, storage array controller 101 may include a
switch 116 coupled to the processing device 104 via a data
communications link 109. The switch 116 may be a computer hardware
device that can create multiple endpoints out of a single endpoint,
thereby enabling multiple devices to share a single endpoint. The
switch 116 may, for example, be a PCIe switch that is coupled to a
PCIe bus (e.g., data communications link 109) and presents multiple
PCIe connection points to the midplane.
In implementations, storage array controller 101 includes a data
communications link 107 for coupling the storage array controller
101 to other storage array controllers. In some examples, data
communications link 107 may be a QuickPath Interconnect (QPI)
interconnect.
A traditional storage system that uses traditional flash drives may
implement a process across the flash drives that are part of the
traditional storage system. For example, a higher level process of
the storage system may initiate and control a process across the
flash drives. However, a flash drive of the traditional storage
system may include its own storage controller that also performs
the process. Thus, for the traditional storage system, a higher
level process (e.g., initiated by the storage system) and a lower
level process (e.g., initiated by a storage controller of the
storage system) may both be performed.
To resolve various deficiencies of a traditional storage system,
operations may be performed by higher level processes and not by
the lower level processes. For example, the flash storage system
may include flash drives that do not include storage controllers
that provide the process. Thus, the operating system of the flash
storage system itself may initiate and control the process. This
may be accomplished by a direct-mapped flash storage system that
addresses data blocks within the flash drives directly and without
an address translation performed by the storage controllers of the
flash drives.
The operating system of the flash storage system may identify and
maintain a list of allocation units across multiple flash drives of
the flash storage system. The allocation units may be entire erase
blocks or multiple erase blocks. The operating system may maintain
a map or address range that directly maps addresses to erase blocks
of the flash drives of the flash storage system.
Direct mapping to the erase blocks of the flash drives may be used
to rewrite data and erase data. For example, the operations may be
performed on one or more allocation units that include a first data
and a second data where the first data is to be retained and the
second data is no longer being used by the flash storage system.
The operating system may initiate the process to write the first
data to new locations within other allocation units and erasing the
second data and marking the allocation units as being available for
use for subsequent data. Thus, the process may only be performed by
the higher level operating system of the flash storage system
without an additional lower level process being performed by
controllers of the flash drives.
Advantages of the process being performed only by the operating
system of the flash storage system include increased reliability of
the flash drives of the flash storage system as unnecessary or
redundant write operations are not being performed during the
process. One possible point of novelty here is the concept of
initiating and controlling the process at the operating system of
the flash storage system. In addition, the process can be
controlled by the operating system across multiple flash drives.
This is contrast to the process being performed by a storage
controller of a flash drive.
A storage system can consist of two storage array controllers that
share a set of drives for failover purposes, or it could consist of
a single storage array controller that provides a storage service
that utilizes multiple drives, or it could consist of a distributed
network of storage array controllers each with some number of
drives or some amount of Flash storage where the storage array
controllers in the network collaborate to provide a complete
storage service and collaborate on various aspects of a storage
service including storage allocation and garbage collection.
FIG. 1C illustrates a third example system 117 for data storage in
accordance with some implementations. System 117 (also referred to
as "storage system" herein) includes numerous elements for purposes
of illustration rather than limitation. It may be noted that system
117 may include the same, more, or fewer elements configured in the
same or different manner in other implementations.
In one embodiment, system 117 includes a dual Peripheral Component
Interconnect (`PCI`) flash storage device 118 with separately
addressable fast write storage. System 117 may include a storage
controller 119. In one embodiment, storage controller 119A-D may be
a CPU, ASIC, FPGA, or any other circuitry that may implement
control structures necessary according to the present disclosure.
In one embodiment, system 117 includes flash memory devices (e.g.,
including flash memory devices 120a-n), operatively coupled to
various channels of the storage device controller 119. Flash memory
devices 120a-n, may be presented to the controller 119A-D as an
addressable collection of Flash pages, erase blocks, and/or control
elements sufficient to allow the storage device controller 119A-D
to program and retrieve various aspects of the Flash. In one
embodiment, storage device controller 119A-D may perform operations
on flash memory devices 120a-n including storing and retrieving
data content of pages, arranging and erasing any blocks, tracking
statistics related to the use and reuse of Flash memory pages,
erase blocks, and cells, tracking and predicting error codes and
faults within the Flash memory, controlling voltage levels
associated with programming and retrieving contents of Flash cells,
etc.
In one embodiment, system 117 may include RAM 121 to store
separately addressable fast-write data. In one embodiment, RAM 121
may be one or more separate discrete devices. In another
embodiment, RAM 121 may be integrated into storage device
controller 119A-D or multiple storage device controllers. The RAM
121 may be utilized for other purposes as well, such as temporary
program memory for a processing device (e.g., a CPU) in the storage
device controller 119.
In one embodiment, system 117 may include a stored energy device
122, such as a rechargeable battery or a capacitor. Stored energy
device 122 may store energy sufficient to power the storage device
controller 119, some amount of the RAM (e.g., RAM 121), and some
amount of Flash memory (e.g., Flash memory 120a-120n) for
sufficient time to write the contents of RAM to Flash memory. In
one embodiment, storage device controller 119A-D may write the
contents of RAM to Flash Memory if the storage device controller
detects loss of external power.
In one embodiment, system 117 includes two data communications
links 123a, 123b. In one embodiment, data communications links
123a, 123b may be PCI interfaces. In another embodiment, data
communications links 123a, 123b may be based on other
communications standards (e.g., HyperTransport, InfiniBand, etc.).
Data communications links 123a, 123b may be based on non-volatile
memory express (`NVMe`) or NVMe over fabrics (`NVMf`)
specifications that allow external connection to the storage device
controller 119A-D from other components in the storage system 117.
It should be noted that data communications links may be
interchangeably referred to herein as PCI buses for
convenience.
System 117 may also include an external power source (not shown),
which may be provided over one or both data communications links
123a, 123b, or which may be provided separately. An alternative
embodiment includes a separate Flash memory (not shown) dedicated
for use in storing the content of RAM 121. The storage device
controller 119A-D may present a logical device over a PCI bus which
may include an addressable fast-write logical device, or a distinct
part of the logical address space of the storage device 118, which
may be presented as PCI memory or as persistent storage. In one
embodiment, operations to store into the device are directed into
the RAM 121. On power failure, the storage device controller 119A-D
may write stored content associated with the addressable fast-write
logical storage to Flash memory (e.g., Flash memory 120a-n) for
long-term persistent storage.
In one embodiment, the logical device may include some presentation
of some or all of the content of the Flash memory devices 120a-n,
where that presentation allows a storage system including a storage
device 118 (e.g., storage system 117) to directly address Flash
memory pages and directly reprogram erase blocks from storage
system components that are external to the storage device through
the PCI bus. The presentation may also allow one or more of the
external components to control and retrieve other aspects of the
Flash memory including some or all of: tracking statistics related
to use and reuse of Flash memory pages, erase blocks, and cells
across all the Flash memory devices; tracking and predicting error
codes and faults within and across the Flash memory devices;
controlling voltage levels associated with programming and
retrieving contents of Flash cells; etc.
In one embodiment, the stored energy device 122 may be sufficient
to ensure completion of in-progress operations to the Flash memory
devices 120a-120n stored energy device 122 may power storage device
controller 119A-D and associated Flash memory devices (e.g.,
120a-n) for those operations, as well as for the storing of
fast-write RAM to Flash memory. Stored energy device 122 may be
used to store accumulated statistics and other parameters kept and
tracked by the Flash memory devices 120a-n and/or the storage
device controller 119. Separate capacitors or stored energy devices
(such as smaller capacitors near or embedded within the Flash
memory devices themselves) may be used for some or all of the
operations described herein.
Various schemes may be used to track and optimize the life span of
the stored energy component, such as adjusting voltage levels over
time, partially discharging the storage energy device 122 to
measure corresponding discharge characteristics, etc. If the
available energy decreases over time, the effective available
capacity of the addressable fast-write storage may be decreased to
ensure that it can be written safely based on the currently
available stored energy.
FIG. 1D illustrates a third example system 124 for data storage in
accordance with some implementations. In one embodiment, system 124
includes storage controllers 125a, 125b. In one embodiment, storage
controllers 125a, 125b are operatively coupled to Dual PCI storage
devices 119a, 119b and 119c, 119d, respectively. Storage
controllers 125a, 125b may be operatively coupled (e.g., via a
storage network 130) to some number of host computers 127a-n.
In one embodiment, two storage controllers (e.g., 125a and 125b)
provide storage services, such as a SCS) block storage array, a
file server, an object server, a database or data analytics
service, etc. The storage controllers 125a, 125b may provide
services through some number of network interfaces (e.g., 126a-d)
to host computers 127a-n outside of the storage system 124. Storage
controllers 125a, 125b may provide integrated services or an
application entirely within the storage system 124, forming a
converged storage and compute system. The storage controllers 125a,
125b may utilize the fast write memory within or across storage
devices 119a-d to journal in progress operations to ensure the
operations are not lost on a power failure, storage controller
removal, storage controller or storage system shutdown, or some
fault of one or more software or hardware components within the
storage system 124.
In one embodiment, controllers 125a, 125b operate as PCI masters to
one or the other PCI buses 128a, 128b. In another embodiment, 128a
and 128b may be based on other communications standards (e.g.,
HyperTransport, InfiniBand, etc.). Other storage system embodiments
may operate storage controllers 125a, 125b as multi-masters for
both PCI buses 128a, 128b. Alternately, a PCI/NVMe/NVMf switching
infrastructure or fabric may connect multiple storage controllers.
Some storage system embodiments may allow storage devices to
communicate with each other directly rather than communicating only
with storage controllers. In one embodiment, a storage device
controller 119a may be operable under direction from a storage
controller 125a to synthesize and transfer data to be stored into
Flash memory devices from data that has been stored in RAM (e.g.,
RAM 121 of FIG. 1C). For example, a recalculated version of RAM
content may be transferred after a storage controller has
determined that an operation has fully committed across the storage
system, or when fast-write memory on the device has reached a
certain used capacity, or after a certain amount of time, to ensure
improve safety of the data or to release addressable fast-write
capacity for reuse. This mechanism may be used, for example, to
avoid a second transfer over a bus (e.g., 128a, 128b) from the
storage controllers 125a, 125b. In one embodiment, a recalculation
may include compressing data, attaching indexing or other metadata,
combining multiple data segments together, performing erasure code
calculations, etc.
In one embodiment, under direction from a storage controller 125a,
125b, a storage device controller 119a, 119b may be operable to
calculate and transfer data to other storage devices from data
stored in RAM (e.g., RAM 121 of FIG. 1C) without involvement of the
storage controllers 125a, 125b. This operation may be used to
mirror data stored in one controller 125a to another controller
125b, or it could be used to offload compression, data aggregation,
and/or erasure coding calculations and transfers to storage devices
to reduce load on storage controllers or the storage controller
interface 129a, 129b to the PCI bus 128a, 128b.
A storage device controller 119A-D may include mechanisms for
implementing high availability primitives for use by other parts of
a storage system external to the Dual PCI storage device 118. For
example, reservation or exclusion primitives may be provided so
that, in a storage system with two storage controllers providing a
highly available storage service, one storage controller may
prevent the other storage controller from accessing or continuing
to access the storage device. This could be used, for example, in
cases where one controller detects that the other controller is not
functioning properly or where the interconnect between the two
storage controllers may itself not be functioning properly.
In one embodiment, a storage system for use with Dual PCI direct
mapped storage devices with separately addressable fast write
storage includes systems that manage erase blocks or groups of
erase blocks as allocation units for storing data on behalf of the
storage service, or for storing metadata (e.g., indexes, logs,
etc.) associated with the storage service, or for proper management
of the storage system itself. Flash pages, which may be a few
kilobytes in size, may be written as data arrives or as the storage
system is to persist data for long intervals of time (e.g., above a
defined threshold of time). To commit data more quickly, or to
reduce the number of writes to the Flash memory devices, the
storage controllers may first write data into the separately
addressable fast write storage on one more storage devices.
In one embodiment, the storage controllers 125a, 125b may initiate
the use of erase blocks within and across storage devices (e.g.,
118) in accordance with an age and expected remaining lifespan of
the storage devices, or based on other statistics. The storage
controllers 125a, 125b may initiate garbage collection and data
migration data between storage devices in accordance with pages
that are no longer needed as well as to manage Flash page and erase
block lifespans and to manage overall system performance.
In one embodiment, the storage system 124 may utilize mirroring
and/or erasure coding schemes as part of storing data into
addressable fast write storage and/or as part of writing data into
allocation units associated with erase blocks. Erasure codes may be
used across storage devices, as well as within erase blocks or
allocation units, or within and across Flash memory devices on a
single storage device, to provide redundancy against single or
multiple storage device failures or to protect against internal
corruptions of Flash memory pages resulting from Flash memory
operations or from degradation of Flash memory cells. Mirroring and
erasure coding at various levels may be used to recover from
multiple types of failures that occur separately or in
combination.
The embodiments depicted with reference to FIGS. 2A-G illustrate a
storage cluster that stores user data, such as user data
originating from one or more user or client systems or other
sources external to the storage cluster. The storage cluster
distributes user data across storage nodes housed within a chassis,
or across multiple chassis, using erasure coding and redundant
copies of metadata. Erasure coding refers to a method of data
protection or reconstruction in which data is stored across a set
of different locations, such as disks, storage nodes or geographic
locations. Flash memory is one type of solid-state memory that may
be integrated with the embodiments, although the embodiments may be
extended to other types of solid-state memory or other storage
medium, including non-solid state memory. Control of storage
locations and workloads are distributed across the storage
locations in a clustered peer-to-peer system. Tasks such as
mediating communications between the various storage nodes,
detecting when a storage node has become unavailable, and balancing
I/Os (inputs and outputs) across the various storage nodes, are all
handled on a distributed basis. Data is laid out or distributed
across multiple storage nodes in data fragments or stripes that
support data recovery in some embodiments. Ownership of data can be
reassigned within a cluster, independent of input and output
patterns. This architecture described in more detail below allows a
storage node in the cluster to fail, with the system remaining
operational, since the data can be reconstructed from other storage
nodes and thus remain available for input and output operations. In
various embodiments, a storage node may be referred to as a cluster
node, a blade, or a server.
The storage cluster may be contained within a chassis, i.e., an
enclosure housing one or more storage nodes. A mechanism to provide
power to each storage node, such as a power distribution bus, and a
communication mechanism, such as a communication bus that enables
communication between the storage nodes are included within the
chassis. The storage cluster can run as an independent system in
one location according to some embodiments. In one embodiment, a
chassis contains at least two instances of both the power
distribution and the communication bus which may be enabled or
disabled independently. The internal communication bus may be an
Ethernet bus, however, other technologies such as PCIe, InfiniBand,
and others, are equally suitable. The chassis provides a port for
an external communication bus for enabling communication between
multiple chassis, directly or through a switch, and with client
systems. The external communication may use a technology such as
Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments, the
external communication bus uses different communication bus
technologies for inter-chassis and client communication. If a
switch is deployed within or between chassis, the switch may act as
a translation between multiple protocols or technologies. When
multiple chassis are connected to define a storage cluster, the
storage cluster may be accessed by a client using either
proprietary interfaces or standard interfaces such as network file
system (`NFS`), common internet file system (`CIFS`), small
computer system interface (`SCSI`) or hypertext transfer protocol
(`HTTP`). Translation from the client protocol may occur at the
switch, chassis external communication bus or within each storage
node. In some embodiments, multiple chassis may be coupled or
connected to each other through an aggregator switch. A portion
and/or all of the coupled or connected chassis may be designated as
a storage cluster. As discussed above, each chassis can have
multiple blades, each blade has a media access control (`MAC`)
address, but the storage cluster is presented to an external
network as having a single cluster IP address and a single MAC
address in some embodiments.
Each storage node may be one or more storage servers and each
storage server is connected to one or more non-volatile solid state
memory units, which may be referred to as storage units or storage
devices. One embodiment includes a single storage server in each
storage node and between one to eight non-volatile solid state
memory units, however this one example is not meant to be limiting.
The storage server may include a processor, DRAM and interfaces for
the internal communication bus and power distribution for each of
the power buses. Inside the storage node, the interfaces and
storage unit share a communication bus, e.g., PCI Express, in some
embodiments. The non-volatile solid state memory units may directly
access the internal communication bus interface through a storage
node communication bus, or request the storage node to access the
bus interface. The non-volatile solid state memory unit contains an
embedded CPU, solid state storage controller, and a quantity of
solid state mass storage, e.g., between 2-32 terabytes (`TB`) in
some embodiments. An embedded volatile storage medium, such as
DRAM, and an energy reserve apparatus are included in the
non-volatile solid state memory unit. In some embodiments, the
energy reserve apparatus is a capacitor, super-capacitor, or
battery that enables transferring a subset of DRAM contents to a
stable storage medium in the case of power loss. In some
embodiments, the non-volatile solid state memory unit is
constructed with a storage class memory, such as phase change or
magnetoresistive random access memory (`MRAM`) that substitutes for
DRAM and enables a reduced power hold-up apparatus.
One of many features of the storage nodes and non-volatile solid
state storage is the ability to proactively rebuild data in a
storage cluster. The storage nodes and non-volatile solid state
storage can determine when a storage node or non-volatile solid
state storage in the storage cluster is unreachable, independent of
whether there is an attempt to read data involving that storage
node or non-volatile solid state storage. The storage nodes and
non-volatile solid state storage then cooperate to recover and
rebuild the data in at least partially new locations. This
constitutes a proactive rebuild, in that the system rebuilds data
without waiting until the data is needed for a read access
initiated from a client system employing the storage cluster. These
and further details of the storage memory and operation thereof are
discussed below.
FIG. 2A is a perspective view of a storage cluster 161, with
multiple storage nodes 150 and internal solid-state memory coupled
to each storage node to provide network attached storage or storage
area network, in accordance with some embodiments. A network
attached storage, storage area network, or a storage cluster, or
other storage memory, could include one or more storage clusters
161, each having one or more storage nodes 150, in a flexible and
reconfigurable arrangement of both the physical components and the
amount of storage memory provided thereby. The storage cluster 161
is designed to fit in a rack, and one or more racks can be set up
and populated as desired for the storage memory. The storage
cluster 161 has a chassis 138 having multiple slots 142. It should
be appreciated that chassis 138 may be referred to as a housing,
enclosure, or rack unit. In one embodiment, the chassis 138 has
fourteen slots 142, although other numbers of slots are readily
devised. For example, some embodiments have four slots, eight
slots, sixteen slots, thirty-two slots, or other suitable number of
slots. Each slot 142 can accommodate one storage node 150 in some
embodiments. Chassis 138 includes flaps 148 that can be utilized to
mount the chassis 138 on a rack. Fans 144 provide air circulation
for cooling of the storage nodes 150 and components thereof,
although other cooling components could be used, or an embodiment
could be devised without cooling components. A switch fabric 146
couples storage nodes 150 within chassis 138 together and to a
network for communication to the memory. In an embodiment depicted
in herein, the slots 142 to the left of the switch fabric 146 and
fans 144 are shown occupied by storage nodes 150, while the slots
142 to the right of the switch fabric 146 and fans 144 are empty
and available for insertion of storage node 150 for illustrative
purposes. This configuration is one example, and one or more
storage nodes 150 could occupy the slots 142 in various further
arrangements. The storage node arrangements need not be sequential
or adjacent in some embodiments. Storage nodes 150 are hot
pluggable, meaning that a storage node 150 can be inserted into a
slot 142 in the chassis 138, or removed from a slot 142, without
stopping or powering down the system. Upon insertion or removal of
storage node 150 from slot 142, the system automatically
reconfigures in order to recognize and adapt to the change.
Reconfiguration, in some embodiments, includes restoring redundancy
and/or rebalancing data or load.
Each storage node 150 can have multiple components. In the
embodiment shown here, the storage node 150 includes a printed
circuit board 159 populated by a CPU 156, i.e., processor, a memory
154 coupled to the CPU 156, and a non-volatile solid state storage
152 coupled to the CPU 156, although other mountings and/or
components could be used in further embodiments. The memory 154 has
instructions which are executed by the CPU 156 and/or data operated
on by the CPU 156. As further explained below, the non-volatile
solid state storage 152 includes flash or, in further embodiments,
other types of solid-state memory.
Referring to FIG. 2A, storage cluster 161 is scalable, meaning that
storage capacity with non-uniform storage sizes is readily added,
as described above. One or more storage nodes 150 can be plugged
into or removed from each chassis and the storage cluster
self-configures in some embodiments. Plug-in storage nodes 150,
whether installed in a chassis as delivered or later added, can
have different sizes. For example, in one embodiment a storage node
150 can have any multiple of 4 TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB,
etc. In further embodiments, a storage node 150 could have any
multiple of other storage amounts or capacities. Storage capacity
of each storage node 150 is broadcast, and influences decisions of
how to stripe the data. For maximum storage efficiency, an
embodiment can self-configure as wide as possible in the stripe,
subject to a predetermined requirement of continued operation with
loss of up to one, or up to two, non-volatile solid state storage
units 152 or storage nodes 150 within the chassis.
FIG. 2B is a block diagram showing a communications interconnect
173 and power distribution bus 172 coupling multiple storage nodes
150. Referring back to FIG. 2A, the communications interconnect 173
can be included in or implemented with the switch fabric 146 in
some embodiments. Where multiple storage clusters 161 occupy a
rack, the communications interconnect 173 can be included in or
implemented with a top of rack switch, in some embodiments. As
illustrated in FIG. 2B, storage cluster 161 is enclosed within a
single chassis 138. External port 176 is coupled to storage nodes
150 through communications interconnect 173, while external port
174 is coupled directly to a storage node. External power port 178
is coupled to power distribution bus 172. Storage nodes 150 may
include varying amounts and differing capacities of non-volatile
solid state storage 152 as described with reference to FIG. 2A. In
addition, one or more storage nodes 150 may be a compute only
storage node as illustrated in FIG. 2B. Authorities 168 are
implemented on the non-volatile solid state storages 152, for
example as lists or other data structures stored in memory. In some
embodiments the authorities are stored within the non-volatile
solid state storage 152 and supported by software executing on a
controller or other processor of the non-volatile solid state
storage 152. In a further embodiment, authorities 168 are
implemented on the storage nodes 150, for example as lists or other
data structures stored in the memory 154 and supported by software
executing on the CPU 156 of the storage node 150. Authorities 168
control how and where data is stored in the non-volatile solid
state storages 152 in some embodiments. This control assists in
determining which type of erasure coding scheme is applied to the
data, and which storage nodes 150 have which portions of the data.
Each authority 168 may be assigned to a non-volatile solid state
storage 152. Each authority may control a range of inode numbers,
segment numbers, or other data identifiers which are assigned to
data by a file system, by the storage nodes 150, or by the
non-volatile solid state storage 152, in various embodiments.
Every piece of data, and every piece of metadata, has redundancy in
the system in some embodiments. In addition, every piece of data
and every piece of metadata has an owner, which may be referred to
as an authority. If that authority is unreachable, for example
through failure of a storage node, there is a plan of succession
for how to find that data or that metadata. In various embodiments,
there are redundant copies of authorities 168. Authorities 168 have
a relationship to storage nodes 150 and non-volatile solid state
storage 152 in some embodiments. Each authority 168, covering a
range of data segment numbers or other identifiers of the data, may
be assigned to a specific non-volatile solid state storage 152. In
some embodiments the authorities 168 for all of such ranges are
distributed over the non-volatile solid state storages 152 of a
storage cluster. Each storage node 150 has a network port that
provides access to the non-volatile solid state storage(s) 152 of
that storage node 150. Data can be stored in a segment, which is
associated with a segment number and that segment number is an
indirection for a configuration of a RAID (redundant array of
independent disks) stripe in some embodiments. The assignment and
use of the authorities 168 thus establishes an indirection to data.
Indirection may be referred to as the ability to reference data
indirectly, in this case via an authority 168, in accordance with
some embodiments. A segment identifies a set of non-volatile solid
state storage 152 and a local identifier into the set of
non-volatile solid state storage 152 that may contain data. In some
embodiments, the local identifier is an offset into the device and
may be reused sequentially by multiple segments. In other
embodiments the local identifier is unique for a specific segment
and never reused. The offsets in the non-volatile solid state
storage 152 are applied to locating data for writing to or reading
from the non-volatile solid state storage 152 (in the form of a
RAID stripe). Data is striped across multiple units of non-volatile
solid state storage 152, which may include or be different from the
non-volatile solid state storage 152 having the authority 168 for a
particular data segment.
If there is a change in where a particular segment of data is
located, e.g., during a data move or a data reconstruction, the
authority 168 for that data segment should be consulted, at that
non-volatile solid state storage 152 or storage node 150 having
that authority 168. In order to locate a particular piece of data,
embodiments calculate a hash value for a data segment or apply an
inode number or a data segment number. The output of this operation
points to a non-volatile solid state storage 152 having the
authority 168 for that particular piece of data. In some
embodiments there are two stages to this operation. The first stage
maps an entity identifier (ID), e.g., a segment number, inode
number, or directory number to an authority identifier. This
mapping may include a calculation such as a hash or a bit mask. The
second stage is mapping the authority identifier to a particular
non-volatile solid state storage 152, which may be done through an
explicit mapping. The operation is repeatable, so that when the
calculation is performed, the result of the calculation repeatably
and reliably points to a particular non-volatile solid state
storage 152 having that authority 168. The operation may include
the set of reachable storage nodes as input. If the set of
reachable non-volatile solid state storage units changes the
optimal set changes. In some embodiments, the persisted value is
the current assignment (which is always true) and the calculated
value is the target assignment the cluster will attempt to
reconfigure towards. This calculation may be used to determine the
optimal non-volatile solid state storage 152 for an authority in
the presence of a set of non-volatile solid state storage 152 that
are reachable and constitute the same cluster. The calculation also
determines an ordered set of peer non-volatile solid state storage
152 that will also record the authority to non-volatile solid state
storage mapping so that the authority may be determined even if the
assigned non-volatile solid state storage is unreachable. A
duplicate or substitute authority 168 may be consulted if a
specific authority 168 is unavailable in some embodiments.
With reference to FIGS. 2A and 2B, two of the many tasks of the CPU
156 on a storage node 150 are to break up write data, and
reassemble read data. When the system has determined that data is
to be written, the authority 168 for that data is located as above.
When the segment ID for data is already determined the request to
write is forwarded to the non-volatile solid state storage 152
currently determined to be the host of the authority 168 determined
from the segment. The host CPU 156 of the storage node 150, on
which the non-volatile solid state storage 152 and corresponding
authority 168 reside, then breaks up or shards the data and
transmits the data out to various non-volatile solid state storage
152. The transmitted data is written as a data stripe in accordance
with an erasure coding scheme. In some embodiments, data is
requested to be pulled, and in other embodiments, data is pushed.
In reverse, when data is read, the authority 168 for the segment ID
containing the data is located as described above. The host CPU 156
of the storage node 150 on which the non-volatile solid state
storage 152 and corresponding authority 168 reside requests the
data from the non-volatile solid state storage and corresponding
storage nodes pointed to by the authority. In some embodiments the
data is read from flash storage as a data stripe. The host CPU 156
of storage node 150 then reassembles the read data, correcting any
errors (if present) according to the appropriate erasure coding
scheme, and forwards the reassembled data to the network. In
further embodiments, some or all of these tasks can be handled in
the non-volatile solid state storage 152. In some embodiments, the
segment host requests the data be sent to storage node 150 by
requesting pages from storage and then sending the data to the
storage node making the original request.
In some systems, for example in UNIX-style file systems, data is
handled with an index node or inode, which specifies a data
structure that represents an object in a file system. The object
could be a file or a directory, for example. Metadata may accompany
the object, as attributes such as permission data and a creation
timestamp, among other attributes. A segment number could be
assigned to all or a portion of such an object in a file system. In
other systems, data segments are handled with a segment number
assigned elsewhere. For purposes of discussion, the unit of
distribution is an entity, and an entity can be a file, a directory
or a segment. That is, entities are units of data or metadata
stored by a storage system. Entities are grouped into sets called
authorities. Each authority has an authority owner, which is a
storage node that has the exclusive right to update the entities in
the authority. In other words, a storage node contains the
authority, and that the authority, in turn, contains entities.
A segment is a logical container of data in accordance with some
embodiments. A segment is an address space between medium address
space and physical flash locations, i.e., the data segment number,
are in this address space. Segments may also contain meta-data,
which enable data redundancy to be restored (rewritten to different
flash locations or devices) without the involvement of higher level
software. In one embodiment, an internal format of a segment
contains client data and medium mappings to determine the position
of that data. Each data segment is protected, e.g., from memory and
other failures, by breaking the segment into a number of data and
parity shards, where applicable. The data and parity shards are
distributed, i.e., striped, across non-volatile solid state storage
152 coupled to the host CPUs 156 (See FIGS. 2E and 2G) in
accordance with an erasure coding scheme. Usage of the term
segments refers to the container and its place in the address space
of segments in some embodiments. Usage of the term stripe refers to
the same set of shards as a segment and includes how the shards are
distributed along with redundancy or parity information in
accordance with some embodiments.
A series of address-space transformations takes place across an
entire storage system. At the top are the directory entries (file
names) which link to an inode. Modes point into medium address
space, where data is logically stored. Medium addresses may be
mapped through a series of indirect mediums to spread the load of
large files, or implement data services like deduplication or
snapshots. Medium addresses may be mapped through a series of
indirect mediums to spread the load of large files, or implement
data services like deduplication or snapshots. Segment addresses
are then translated into physical flash locations. Physical flash
locations have an address range bounded by the amount of flash in
the system in accordance with some embodiments. Medium addresses
and segment addresses are logical containers, and in some
embodiments use a 128 bit or larger identifier so as to be
practically infinite, with a likelihood of reuse calculated as
longer than the expected life of the system. Addresses from logical
containers are allocated in a hierarchical fashion in some
embodiments. Initially, each non-volatile solid state storage unit
152 may be assigned a range of address space. Within this assigned
range, the non-volatile solid state storage 152 is able to allocate
addresses without synchronization with other non-volatile solid
state storage 152.
Data and metadata is stored by a set of underlying storage layouts
that are optimized for varying workload patterns and storage
devices. These layouts incorporate multiple redundancy schemes,
compression formats and index algorithms. Some of these layouts
store information about authorities and authority masters, while
others store file metadata and file data. The redundancy schemes
include error correction codes that tolerate corrupted bits within
a single storage device (such as a NAND flash chip), erasure codes
that tolerate the failure of multiple storage nodes, and
replication schemes that tolerate data center or regional failures.
In some embodiments, low density parity check (`LDPC`) code is used
within a single storage unit. Reed-Solomon encoding is used within
a storage cluster, and mirroring is used within a storage grid in
some embodiments. Metadata may be stored using an ordered log
structured index (such as a Log Structured Merge Tree), and large
data may not be stored in a log structured layout.
In order to maintain consistency across multiple copies of an
entity, the storage nodes agree implicitly on two things through
calculations: (1) the authority that contains the entity, and (2)
the storage node that contains the authority. The assignment of
entities to authorities can be done by pseudo randomly assigning
entities to authorities, by splitting entities into ranges based
upon an externally produced key, or by placing a single entity into
each authority. Examples of pseudorandom schemes are linear hashing
and the Replication Under Scalable Hashing (`RUSH`) family of
hashes, including Controlled Replication Under Scalable Hashing
(`CRUSH`). In some embodiments, pseudo-random assignment is
utilized only for assigning authorities to nodes because the set of
nodes can change. The set of authorities cannot change so any
subjective function may be applied in these embodiments. Some
placement schemes automatically place authorities on storage nodes,
while other placement schemes rely on an explicit mapping of
authorities to storage nodes. In some embodiments, a pseudorandom
scheme is utilized to map from each authority to a set of candidate
authority owners. A pseudorandom data distribution function related
to CRUSH may assign authorities to storage nodes and create a list
of where the authorities are assigned. Each storage node has a copy
of the pseudorandom data distribution function, and can arrive at
the same calculation for distributing, and later finding or
locating an authority. Each of the pseudorandom schemes requires
the reachable set of storage nodes as input in some embodiments in
order to conclude the same target nodes. Once an entity has been
placed in an authority, the entity may be stored on physical
devices so that no expected failure will lead to unexpected data
loss. In some embodiments, rebalancing algorithms attempt to store
the copies of all entities within an authority in the same layout
and on the same set of machines.
Examples of expected failures include device failures, stolen
machines, datacenter fires, and regional disasters, such as nuclear
or geological events. Different failures lead to different levels
of acceptable data loss. In some embodiments, a stolen storage node
impacts neither the security nor the reliability of the system,
while depending on system configuration, a regional event could
lead to no loss of data, a few seconds or minutes of lost updates,
or even complete data loss.
In the embodiments, the placement of data for storage redundancy is
independent of the placement of authorities for data consistency.
In some embodiments, storage nodes that contain authorities do not
contain any persistent storage. Instead, the storage nodes are
connected to non-volatile solid state storage units that do not
contain authorities. The communications interconnect between
storage nodes and non-volatile solid state storage units consists
of multiple communication technologies and has non-uniform
performance and fault tolerance characteristics. In some
embodiments, as mentioned above, non-volatile solid state storage
units are connected to storage nodes via PCI express, storage nodes
are connected together within a single chassis using Ethernet
backplane, and chassis are connected together to form a storage
cluster. Storage clusters are connected to clients using Ethernet
or fiber channel in some embodiments. If multiple storage clusters
are configured into a storage grid, the multiple storage clusters
are connected using the Internet or other long-distance networking
links, such as a "metro scale" link or private link that does not
traverse the internet.
Authority owners have the exclusive right to modify entities, to
migrate entities from one non-volatile solid state storage unit to
another non-volatile solid state storage unit, and to add and
remove copies of entities. This allows for maintaining the
redundancy of the underlying data. When an authority owner fails,
is going to be decommissioned, or is overloaded, the authority is
transferred to a new storage node. Transient failures make it
non-trivial to ensure that all non-faulty machines agree upon the
new authority location. The ambiguity that arises due to transient
failures can be achieved automatically by a consensus protocol such
as Paxos, hot-warm failover schemes, via manual intervention by a
remote system administrator, or by a local hardware administrator
(such as by physically removing the failed machine from the
cluster, or pressing a button on the failed machine). In some
embodiments, a consensus protocol is used, and failover is
automatic. If too many failures or replication events occur in too
short a time period, the system goes into a self-preservation mode
and halts replication and data movement activities until an
administrator intervenes in accordance with some embodiments.
As authorities are transferred between storage nodes and authority
owners update entities in their authorities, the system transfers
messages between the storage nodes and non-volatile solid state
storage units. With regard to persistent messages, messages that
have different purposes are of different types. Depending on the
type of the message, the system maintains different ordering and
durability guarantees. As the persistent messages are being
processed, the messages are temporarily stored in multiple durable
and non-durable storage hardware technologies. In some embodiments,
messages are stored in RAM, NVRAM and on NAND flash devices, and a
variety of protocols are used in order to make efficient use of
each storage medium. Latency-sensitive client requests may be
persisted in replicated NVRAM, and then later NAND, while
background rebalancing operations are persisted directly to
NAND.
Persistent messages are persistently stored prior to being
transmitted. This allows the system to continue to serve client
requests despite failures and component replacement. Although many
hardware components contain unique identifiers that are visible to
system administrators, manufacturer, hardware supply chain and
ongoing monitoring quality control infrastructure, applications
running on top of the infrastructure address virtualize addresses.
These virtualized addresses do not change over the lifetime of the
storage system, regardless of component failures and replacements.
This allows each component of the storage system to be replaced
over time without reconfiguration or disruptions of client request
processing, i.e., the system supports non-disruptive upgrades.
In some embodiments, the virtualized addresses are stored with
sufficient redundancy. A continuous monitoring system correlates
hardware and software status and the hardware identifiers. This
allows detection and prediction of failures due to faulty
components and manufacturing details. The monitoring system also
enables the proactive transfer of authorities and entities away
from impacted devices before failure occurs by removing the
component from the critical path in some embodiments.
FIG. 2C is a multiple level block diagram, showing contents of a
storage node 150 and contents of a non-volatile solid state storage
152 of the storage node 150. Data is communicated to and from the
storage node 150 by a network interface controller (`NIC`) 202 in
some embodiments. Each storage node 150 has a CPU 156, and one or
more non-volatile solid state storage 152, as discussed above.
Moving down one level in FIG. 2C, each non-volatile solid state
storage 152 has a relatively fast non-volatile solid state memory,
such as nonvolatile random access memory (`NVRAM`) 204, and flash
memory 206. In some embodiments, NVRAM 204 may be a component that
does not require program/erase cycles (DRAM, MRAM, PCM), and can be
a memory that can support being written vastly more often than the
memory is read from. Moving down another level in FIG. 2C, the
NVRAM 204 is implemented in one embodiment as high speed volatile
memory, such as dynamic random access memory (DRAM) 216, backed up
by energy reserve 218. Energy reserve 218 provides sufficient
electrical power to keep the DRAM 216 powered long enough for
contents to be transferred to the flash memory 206 in the event of
power failure. In some embodiments, energy reserve 218 is a
capacitor, super-capacitor, battery, or other device, that supplies
a suitable supply of energy sufficient to enable the transfer of
the contents of DRAM 216 to a stable storage medium in the case of
power loss. The flash memory 206 is implemented as multiple flash
dies 222, which may be referred to as packages of flash dies 222 or
an array of flash dies 222. It should be appreciated that the flash
dies 222 could be packaged in any number of ways, with a single die
per package, multiple dies per package (i.e. multichip packages),
in hybrid packages, as bare dies on a printed circuit board or
other substrate, as encapsulated dies, etc. In the embodiment
shown, the non-volatile solid state storage 152 has a controller
212 or other processor, and an input output (I/O) port 210 coupled
to the controller 212. I/O port 210 is coupled to the CPU 156
and/or the network interface controller 202 of the flash storage
node 150. Flash input output (I/O) port 220 is coupled to the flash
dies 222, and a direct memory access unit (DMA) 214 is coupled to
the controller 212, the DRAM 216 and the flash dies 222. In the
embodiment shown, the I/O port 210, controller 212, DMA unit 214
and flash I/O port 220 are implemented on a programmable logic
device (`PLD`) 208, e.g., a field programmable gate array (FPGA).
In this embodiment, each flash die 222 has pages, organized as
sixteen kB (kilobyte) pages 224, and a register 226 through which
data can be written to or read from the flash die 222. In further
embodiments, other types of solid-state memory are used in place
of, or in addition to flash memory illustrated within flash die
222.
Storage clusters 161, in various embodiments as disclosed herein,
can be contrasted with storage arrays in general. The storage nodes
150 are part of a collection that creates the storage cluster 161.
Each storage node 150 owns a slice of data and computing required
to provide the data. Multiple storage nodes 150 cooperate to store
and retrieve the data. Storage memory or storage devices, as used
in storage arrays in general, are less involved with processing and
manipulating the data. Storage memory or storage devices in a
storage array receive commands to read, write, or erase data. The
storage memory or storage devices in a storage array are not aware
of a larger system in which they are embedded, or what the data
means. Storage memory or storage devices in storage arrays can
include various types of storage memory, such as RAM, solid state
drives, hard disk drives, etc. The storage units 152 described
herein have multiple interfaces active simultaneously and serving
multiple purposes. In some embodiments, some of the functionality
of a storage node 150 is shifted into a storage unit 152,
transforming the storage unit 152 into a combination of storage
unit 152 and storage node 150. Placing computing (relative to
storage data) into the storage unit 152 places this computing
closer to the data itself. The various system embodiments have a
hierarchy of storage node layers with different capabilities. By
contrast, in a storage array, a controller owns and knows
everything about all of the data that the controller manages in a
shelf or storage devices. In a storage cluster 161, as described
herein, multiple controllers in multiple storage units 152 and/or
storage nodes 150 cooperate in various ways (e.g., for erasure
coding, data sharding, metadata communication and redundancy,
storage capacity expansion or contraction, data recovery, and so
on).
FIG. 2D shows a storage server environment, which uses embodiments
of the storage nodes 150 and storage units 152 of FIGS. 2A-C. In
this version, each storage unit 152 has a processor such as
controller 212 (see FIG. 2C), an FPGA (field programmable gate
array), flash memory 206, and NVRAM 204 (which is super-capacitor
backed DRAM 216, see FIGS. 2B and 2C) on a PCIe (peripheral
component interconnect express) board in a chassis 138 (see FIG.
2A). The storage unit 152 may be implemented as a single board
containing storage, and may be the largest tolerable failure domain
inside the chassis. In some embodiments, up to two storage units
152 may fail and the device will continue with no data loss.
The physical storage is divided into named regions based on
application usage in some embodiments. The NVRAM 204 is a
contiguous block of reserved memory in the storage unit 152 DRAM
216, and is backed by NAND flash. NVRAM 204 is logically divided
into multiple memory regions written for two as spool (e.g.,
spool_region). Space within the NVRAM 204 spools is managed by each
authority 168 independently. Each device provides an amount of
storage space to each authority 168. That authority 168 further
manages lifetimes and allocations within that space. Examples of a
spool include distributed transactions or notions. When the primary
power to a storage unit 152 fails, onboard super-capacitors provide
a short duration of power hold up. During this holdup interval, the
contents of the NVRAM 204 are flushed to flash memory 206. On the
next power-on, the contents of the NVRAM 204 are recovered from the
flash memory 206.
As for the storage unit controller, the responsibility of the
logical "controller" is distributed across each of the blades
containing authorities 168. This distribution of logical control is
shown in FIG. 2D as a host controller 242, mid-tier controller 244
and storage unit controller(s) 246. Management of the control plane
and the storage plane are treated independently, although parts may
be physically co-located on the same blade. Each authority 168
effectively serves as an independent controller. Each authority 168
provides its own data and metadata structures, its own background
workers, and maintains its own lifecycle.
FIG. 2E is a blade 252 hardware block diagram, showing a control
plane 254, compute and storage planes 256, 258, and authorities 168
interacting with underlying physical resources, using embodiments
of the storage nodes 150 and storage units 152 of FIGS. 2A-C in the
storage server environment of FIG. 2D. The control plane 254 is
partitioned into a number of authorities 168 which can use the
compute resources in the compute plane 256 to run on any of the
blades 252. The storage plane 258 is partitioned into a set of
devices, each of which provides access to flash 206 and NVRAM 204
resources. In one embodiment, the compute plane 256 may perform the
operations of a storage array controller, as described herein, on
one or more devices of the storage plane 258 (e.g., a storage
array).
In the compute and storage planes 256, 258 of FIG. 2E, the
authorities 168 interact with the underlying physical resources
(i.e., devices). From the point of view of an authority 168, its
resources are striped over all of the physical devices. From the
point of view of a device, it provides resources to all authorities
168, irrespective of where the authorities happen to run. Each
authority 168 has allocated or has been allocated one or more
partitions 260 of storage memory in the storage units 152, e.g.
partitions 260 in flash memory 206 and NVRAM 204. Each authority
168 uses those allocated partitions 260 that belong to it, for
writing or reading user data. Authorities can be associated with
differing amounts of physical storage of the system. For example,
one authority 168 could have a larger number of partitions 260 or
larger sized partitions 260 in one or more storage units 152 than
one or more other authorities 168.
FIG. 2F depicts elasticity software layers in blades 252 of a
storage cluster, in accordance with some embodiments. In the
elasticity structure, elasticity software is symmetric, i.e., each
blade's compute module 270 runs the three identical layers of
processes depicted in FIG. 2F. Storage managers 274 execute read
and write requests from other blades 252 for data and metadata
stored in local storage unit 152 NVRAM 204 and flash 206.
Authorities 168 fulfill client requests by issuing the necessary
reads and writes to the blades 252 on whose storage units 152 the
corresponding data or metadata resides. Endpoints 272 parse client
connection requests received from switch fabric 146 supervisory
software, relay the client connection requests to the authorities
168 responsible for fulfillment, and relay the authorities' 168
responses to clients. The symmetric three-layer structure enables
the storage system's high degree of concurrency. Elasticity scales
out efficiently and reliably in these embodiments. In addition,
elasticity implements a unique scale-out technique that balances
work evenly across all resources regardless of client access
pattern, and maximizes concurrency by eliminating much of the need
for inter-blade coordination that typically occurs with
conventional distributed locking.
Still referring to FIG. 2F, authorities 168 running in the compute
modules 270 of a blade 252 perform the internal operations required
to fulfill client requests. One feature of elasticity is that
authorities 168 are stateless, i.e., they cache active data and
metadata in their own blades' 252 DRAMs for fast access, but the
authorities store every update in their NVRAM 204 partitions on
three separate blades 252 until the update has been written to
flash 206. All the storage system writes to NVRAM 204 are in
triplicate to partitions on three separate blades 252 in some
embodiments. With triple-mirrored NVRAM 204 and persistent storage
protected by parity and Reed-Solomon RAID checksums, the storage
system can survive concurrent failure of two blades 252 with no
loss of data, metadata, or access to either.
Because authorities 168 are stateless, they can migrate between
blades 252. Each authority 168 has a unique identifier. NVRAM 204
and flash 206 partitions are associated with authorities' 168
identifiers, not with the blades 252 on which they are running in
some. Thus, when an authority 168 migrates, the authority 168
continues to manage the same storage partitions from its new
location. When a new blade 252 is installed in an embodiment of the
storage cluster, the system automatically rebalances load by:
partitioning the new blade's 252 storage for use by the system's
authorities 168, migrating selected authorities 168 to the new
blade 252, starting endpoints 272 on the new blade 252 and
including them in the switch fabric's 146 client connection
distribution algorithm.
From their new locations, migrated authorities 168 persist the
contents of their NVRAM 204 partitions on flash 206, process read
and write requests from other authorities 168, and fulfill the
client requests that endpoints 272 direct to them. Similarly, if a
blade 252 fails or is removed, the system redistributes its
authorities 168 among the system's remaining blades 252. The
redistributed authorities 168 continue to perform their original
functions from their new locations.
FIG. 2G depicts authorities 168 and storage resources in blades 252
of a storage cluster, in accordance with some embodiments. Each
authority 168 is exclusively responsible for a partition of the
flash 206 and NVRAM 204 on each blade 252. The authority 168
manages the content and integrity of its partitions independently
of other authorities 168. Authorities 168 compress incoming data
and preserve it temporarily in their NVRAM 204 partitions, and then
consolidate, RAID-protect, and persist the data in segments of the
storage in their flash 206 partitions. As the authorities 168 write
data to flash 206, storage managers 274 perform the necessary flash
translation to optimize write performance and maximize media
longevity. In the background, authorities 168 "garbage collect," or
reclaim space occupied by data that clients have made obsolete by
overwriting the data. It should be appreciated that since
authorities' 168 partitions are disjoint, there is no need for
distributed locking to execute client and writes or to perform
background functions.
The embodiments described herein may utilize various software,
communication and/or networking protocols. In addition, the
configuration of the hardware and/or software may be adjusted to
accommodate various protocols. For example, the embodiments may
utilize Active Directory, which is a database based system that
provides authentication, directory, policy, and other services in a
WINDOWS.TM. environment. In these embodiments, LDAP (Lightweight
Directory Access Protocol) is one example application protocol for
querying and modifying items in directory service providers such as
Active Directory. In some embodiments, a network lock manager
(`NLM`) is utilized as a facility that works in cooperation with
the Network File System (`NFS`) to provide a System V style of
advisory file and record locking over a network. The Server Message
Block (`SMB`) protocol, one version of which is also known as
Common Internet File System (`CIFS`), may be integrated with the
storage systems discussed herein. SMP operates as an
application-layer network protocol typically used for providing
shared access to files, printers, and serial ports and
miscellaneous communications between nodes on a network. SMB also
provides an authenticated inter-process communication mechanism.
AMAZON.TM. S3 (Simple Storage Service) is a web service offered by
Amazon Web Services, and the systems described herein may interface
with Amazon S3 through web services interfaces (REST
(representational state transfer), SOAP (simple object access
protocol), and BitTorrent). A RESTful API (application programming
interface) breaks down a transaction to create a series of small
modules. Each module addresses a particular underlying part of the
transaction. The control or permissions provided with these
embodiments, especially for object data, may include utilization of
an access control list (`ACL`). The ACL is a list of permissions
attached to an object and the ACL specifies which users or system
processes are granted access to objects, as well as what operations
are allowed on given objects. The systems may utilize Internet
Protocol version 6 (`IPv6`), as well as IPv4, for the
communications protocol that provides an identification and
location system for computers on networks and routes traffic across
the Internet. The routing of packets between networked systems may
include Equal-cost multi-path routing (`ECMP`), which is a routing
strategy where next-hop packet forwarding to a single destination
can occur over multiple "best paths" which tie for top place in
routing metric calculations. Multi-path routing can be used in
conjunction with most routing protocols, because it is a per-hop
decision limited to a single router. The software may support
Multi-tenancy, which is an architecture in which a single instance
of a software application serves multiple customers. Each customer
may be referred to as a tenant. Tenants may be given the ability to
customize some parts of the application, but may not customize the
application's code, in some embodiments. The embodiments may
maintain audit logs. An audit log is a document that records an
event in a computing system. In addition to documenting what
resources were accessed, audit log entries typically include
destination and source addresses, a timestamp, and user login
information for compliance with various regulations. The
embodiments may support various key management policies, such as
encryption key rotation. In addition, the system may support
dynamic root passwords or some variation dynamically changing
passwords.
FIG. 3A sets forth a diagram of a storage system 306 that is
coupled for data communications with a cloud services provider 302
in accordance with some embodiments of the present disclosure.
Although depicted in less detail, the storage system 306 depicted
in FIG. 3A may be similar to the storage systems described above
with reference to FIGS. 1A-1D and FIGS. 2A-2G. In some embodiments,
the storage system 306 depicted in FIG. 3A may be embodied as a
storage system that includes imbalanced active/active controllers,
as a storage system that includes balanced active/active
controllers, as a storage system that includes active/active
controllers where less than all of each controller's resources are
utilized such that each controller has reserve resources that may
be used to support failover, as a storage system that includes
fully active/active controllers, as a storage system that includes
dataset-segregated controllers, as a storage system that includes
dual-layer architectures with front-end controllers and back-end
integrated storage controllers, as a storage system that includes
scale-out clusters of dual-controller arrays, as well as
combinations of such embodiments.
In the example depicted in FIG. 3A, the storage system 306 is
coupled to the cloud services provider 302 via a data
communications link 304. The data communications link 304 may be
embodied as a dedicated data communications link, as a data
communications pathway that is provided through the use of one or
data communications networks such as a wide area network (`WAN`) or
local area network (`LAN`), or as some other mechanism capable of
transporting digital information between the storage system 306 and
the cloud services provider 302. Such a data communications link
304 may be fully wired, fully wireless, or some aggregation of
wired and wireless data communications pathways. In such an
example, digital information may be exchanged between the storage
system 306 and the cloud services provider 302 via the data
communications link 304 using one or more data communications
protocols. For example, digital information may be exchanged
between the storage system 306 and the cloud services provider 302
via the data communications link 304 using the handheld device
transfer protocol (`HDTP`), hypertext transfer protocol (`HTTP`),
internet protocol (`IP`), real-time transfer protocol (`RTP`),
transmission control protocol (`TCP`), user datagram protocol
(`UDP`), wireless application protocol (`WAP`), or other
protocol.
The cloud services provider 302 depicted in FIG. 3A may be
embodied, for example, as a system and computing environment that
provides a vast array of services to users of the cloud services
provider 302 through the sharing of computing resources via the
data communications link 304. The cloud services provider 302 may
provide on-demand access to a shared pool of configurable computing
resources such as computer networks, servers, storage, applications
and services, and so on. The shared pool of configurable resources
may be rapidly provisioned and released to a user of the cloud
services provider 302 with minimal management effort. Generally,
the user of the cloud services provider 302 is unaware of the exact
computing resources utilized by the cloud services provider 302 to
provide the services. Although in many cases such a cloud services
provider 302 may be accessible via the Internet, readers of skill
in the art will recognize that any system that abstracts the use of
shared resources to provide services to a user through any data
communications link may be considered a cloud services provider
302.
In the example depicted in FIG. 3A, the cloud services provider 302
may be configured to provide a variety of services to the storage
system 306 and users of the storage system 306 through the
implementation of various service models. For example, the cloud
services provider 302 may be configured to provide services through
the implementation of an infrastructure as a service (`IaaS`)
service model where the cloud services provider 302 offers
computing infrastructure such as virtual machines and other
resources as a service to subscribers, through the implementation
of a platform as a service (`PaaS`) service model where the cloud
services provider 302 offers a development environment to
application developers, or in other ways. Such a development
environment may include, for example, an operating system,
programming-language execution environment, database, web server,
or other components that may be utilized by application developers
to develop and run software solutions on a cloud platform.
Furthermore, the cloud services provider 302 may be configured to
provide services through the implementation of a software as a
service (`SaaS`) service model where the cloud services provider
302 offers application software, databases, as well as the
platforms that are used to run the applications to the storage
system 306 and users of the storage system 306, providing the
storage system 306 and users of the storage system 306 with
on-demand software and eliminating the need to install and run the
application on local computers, which may simplify maintenance and
support of the application. The cloud services provider 302 may be
further configured to provide services through the implementation
of an authentication as a service (`AaaS`) service model where the
cloud services provider 302 offers authentication services that can
be used to secure access to applications, data sources, or other
resources. The cloud services provider 302 may also be configured
to provide services to the storage system 306 and users of the
storage system 306 through the implementation of a storage as a
service model where the cloud services provider 302 offers access
to its storage infrastructure for use by the storage system 306 and
users of the storage system 306. Readers will appreciate that the
cloud services provider 302 may be configured to provide additional
services to the storage system 306 and users of the storage system
306 through the implementation of additional service models, as the
service models described above are included only for explanatory
purposes and in no way represent a limitation of the services that
may be offered by the cloud services provider 302 or a limitation
as to the service models that may be implemented by the cloud
services provider 302.
In the example depicted in FIG. 3A, the cloud services provider 302
may be embodied, for example, as a private cloud, as a public
cloud, or as a combination of a private cloud and public cloud. In
an embodiment in which the cloud services provider 302 is embodied
as a private cloud, the cloud services provider 302 may be
dedicated to providing services to a single organization rather
than providing services to multiple organizations. In an embodiment
where the cloud services provider 302 is embodied as a public
cloud, the cloud services provider 302 may provide services to
multiple organizations. Public cloud and private cloud deployment
models may differ and may come with various advantages and
disadvantages. For example, because a public cloud deployment
involves the sharing of a computing infrastructure across different
organization, such a deployment may not be ideal for organizations
with security concerns, mission-critical workloads, uptime
requirements demands, and so on. While a private cloud deployment
can address some of these issues, a private cloud deployment may
require on-premises staff to manage the private cloud. In still
alternative embodiments, the cloud services provider 302 may be
embodied as a mix of a private and public cloud services with a
hybrid cloud deployment.
Although not explicitly depicted in FIG. 3A, readers will
appreciate that a vast amount of additional hardware components and
additional software components may be necessary to facilitate the
delivery of cloud services to the storage system 306 and users of
the storage system 306. For example, the storage system 306 may be
coupled to (or even include) a cloud storage gateway. Such a cloud
storage gateway may be embodied, for example, as hardware-based or
software-based appliance that is located on premise with the
storage system 306. Such a cloud storage gateway may operate as a
bridge between local applications that are executing on the storage
array 306 and remote, cloud-based storage that is utilized by the
storage array 306. Through the use of a cloud storage gateway,
organizations may move primary iSCSI or NAS to the cloud services
provider 302, thereby enabling the organization to save space on
their on-premises storage systems. Such a cloud storage gateway may
be configured to emulate a disk array, a block-based device, a file
server, or other storage system that can translate the SCSI
commands, file server commands, or other appropriate command into
REST-space protocols that facilitate communications with the cloud
services provider 302.
In order to enable the storage system 306 and users of the storage
system 306 to make use of the services provided by the cloud
services provider 302, a cloud migration process may take place
during which data, applications, or other elements from an
organization's local systems (or even from another cloud
environment) are moved to the cloud services provider 302. In order
to successfully migrate data, applications, or other elements to
the cloud services provider's 302 environment, middleware such as a
cloud migration tool may be utilized to bridge gaps between the
cloud services provider's 302 environment and an organization's
environment. Such cloud migration tools may also be configured to
address potentially high network costs and long transfer times
associated with migrating large volumes of data to the cloud
services provider 302, as well as addressing security concerns
associated with sensitive data to the cloud services provider 302
over data communications networks. In order to further enable the
storage system 306 and users of the storage system 306 to make use
of the services provided by the cloud services provider 302, a
cloud orchestrator may also be used to arrange and coordinate
automated tasks in pursuit of creating a consolidated process or
workflow. Such a cloud orchestrator may perform tasks such as
configuring various components, whether those components are cloud
components or on-premises components, as well as managing the
interconnections between such components. The cloud orchestrator
can simplify the inter-component communication and connections to
ensure that links are correctly configured and maintained.
In the example depicted in FIG. 3A, and as described briefly above,
the cloud services provider 302 may be configured to provide
services to the storage system 306 and users of the storage system
306 through the usage of a SaaS service model where the cloud
services provider 302 offers application software, databases, as
well as the platforms that are used to run the applications to the
storage system 306 and users of the storage system 306, providing
the storage system 306 and users of the storage system 306 with
on-demand software and eliminating the need to install and run the
application on local computers, which may simplify maintenance and
support of the application. Such applications may take many forms
in accordance with various embodiments of the present disclosure.
For example, the cloud services provider 302 may be configured to
provide access to data analytics applications to the storage system
306 and users of the storage system 306. Such data analytics
applications may be configured, for example, to receive vast
amounts of telemetry data phoned home by the storage system 306.
Such telemetry data may describe various operating characteristics
of the storage system 306 and may be analyzed for a vast array of
purposes including, for example, to determine the health of the
storage system 306, to identify workloads that are executing on the
storage system 306, to predict when the storage system 306 will run
out of various resources, to recommend configuration changes,
hardware or software upgrades, workflow migrations, or other
actions that may improve the operation of the storage system
306.
The cloud services provider 302 may also be configured to provide
access to virtualized computing environments to the storage system
306 and users of the storage system 306. Such virtualized computing
environments may be embodied, for example, as a virtual machine or
other virtualized computer hardware platforms, virtual storage
devices, virtualized computer network resources, and so on.
Examples of such virtualized environments can include virtual
machines that are created to emulate an actual computer,
virtualized desktop environments that separate a logical desktop
from a physical machine, virtualized file systems that allow
uniform access to different types of concrete file systems, and
many others.
For further explanation, FIG. 3B sets forth a diagram of a storage
system 306 in accordance with some embodiments of the present
disclosure. Although depicted in less detail, the storage system
306 depicted in FIG. 3B may be similar to the storage systems
described above with reference to FIGS. 1A-1D and FIGS. 2A-2G as
the storage system may include many of the components described
above.
The storage system 306 depicted in FIG. 3B may include a vast
amount of storage resources 308, which may be embodied in many
forms. For example, the storage resources 308 can include nano-RAM
or another form of nonvolatile random access memory that utilizes
carbon nanotubes deposited on a substrate, 3D crosspoint
non-volatile memory, flash memory including single-level cell
(`SLC`) NAND flash, multi-level cell (`MLC`) NAND flash,
triple-level cell (`TLC`) NAND flash, quad-level cell (`QLC`) NAND
flash, or others. Likewise, the storage resources 308 may include
non-volatile magnetoresistive random-access memory (`MRAM`),
including spin transfer torque (`STT`) MRAM. The example storage
resources 308 may alternatively include non-volatile phase-change
memory (`PCM`), quantum memory that allows for the storage and
retrieval of photonic quantum information, resistive random-access
memory (`ReRAM`), storage class memory (`SCM`), or other form of
storage resources, including any combination of resources described
herein. Readers will appreciate that other forms of computer
memories and storage devices may be utilized by the storage systems
described above, including DRAM, SRAM, EEPROM, universal memory,
and many others. The storage resources 308 depicted in FIG. 3A may
be embodied in a variety of form factors, including but not limited
to, dual in-line memory modules (`DIMMs`), non-volatile dual
in-line memory modules (`NVDIMMs`), M.2, U.2, and others.
The storage resources 308 depicted in FIG. 3A may include various
forms of storage-class memory (`SCM`). SCM may effectively treat
fast, non-volatile memory (e.g., NAND flash) as an extension of
DRAM such that an entire dataset may be treated as an in-memory
dataset that resides entirely in DRAM. SCM may include non-volatile
media such as, for example, NAND flash. Such NAND flash may be
accessed utilizing NVMe that can use the PCIe bus as its transport,
providing for relatively low access latencies compared to older
protocols. In fact, the network protocols used for SSDs in
all-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP),
Fibre Channel (NVMe FC), InfiniBand (iWARP), and others that make
it possible to treat fast, non-volatile memory as an extension of
DRAM. In view of the fact that DRAM is often byte-addressable and
fast, non-volatile memory such as NAND flash is block-addressable,
a controller software/hardware stack may be needed to convert the
block data to the bytes that are stored in the media. Examples of
media and software that may be used as SCM can include, for
example, 3D XPoint, Intel Memory Drive Technology, Samsung's Z-SSD,
and others.
The example storage system 306 depicted in FIG. 3B may implement a
variety of storage architectures. For example, storage systems in
accordance with some embodiments of the present disclosure may
utilize block storage where data is stored in blocks, and each
block essentially acts as an individual hard drive. Storage systems
in accordance with some embodiments of the present disclosure may
utilize object storage, where data is managed as objects. Each
object may include the data itself, a variable amount of metadata,
and a globally unique identifier, where object storage can be
implemented at multiple levels (e.g., device level, system level,
interface level). Storage systems in accordance with some
embodiments of the present disclosure utilize file storage in which
data is stored in a hierarchical structure. Such data may be saved
in files and folders, and presented to both the system storing it
and the system retrieving it in the same format.
The example storage system 306 depicted in FIG. 3B may be embodied
as a storage system in which additional storage resources can be
added through the use of a scale-up model, additional storage
resources can be added through the use of a scale-out model, or
through some combination thereof. In a scale-up model, additional
storage may be added by adding additional storage devices. In a
scale-out model, however, additional storage nodes may be added to
a cluster of storage nodes, where such storage nodes can include
additional processing resources, additional networking resources,
and so on.
The storage system 306 depicted in FIG. 3B also includes
communications resources 310 that may be useful in facilitating
data communications between components within the storage system
306, as well as data communications between the storage system 306
and computing devices that are outside of the storage system 306,
including embodiments where those resources are separated by a
relatively vast expanse. The communications resources 310 may be
configured to utilize a variety of different protocols and data
communication fabrics to facilitate data communications between
components within the storage systems as well as computing devices
that are outside of the storage system. For example, the
communications resources 310 can include fibre channel (`FC`)
technologies such as FC fabrics and FC protocols that can transport
SCSI commands over FC networks. The communications resources 310
can also include FC over ethernet (`FCoE`) technologies through
which FC frames are encapsulated and transmitted over Ethernet
networks. The communications resources 310 can also include
InfiniBand (`IB`) technologies in which a switched fabric topology
is utilized to facilitate transmissions between channel adapters.
The communications resources 310 can also include NVM Express
(`NVMe`) technologies and NVMe over fabrics (`NVMeoF`) technologies
through which non-volatile storage media attached via a PCI express
(`PCIe`) bus may be accessed. The communications resources 310 can
also include mechanisms for accessing storage resources 308 within
the storage system 306 utilizing serial attached SCSI (`SAS`),
serial ATA (`SATA`) bus interfaces for connecting storage resources
308 within the storage system 306 to host bus adapters within the
storage system 306, internet small computer systems interface
(`iSCSI`) technologies to provide block-level access to storage
resources 308 within the storage system 306, and other
communications resources that that may be useful in facilitating
data communications between components within the storage system
306, as well as data communications between the storage system 306
and computing devices that are outside of the storage system
306.
The storage system 306 depicted in FIG. 3B also includes processing
resources 312 that may be useful in useful in executing computer
program instructions and performing other computational tasks
within the storage system 306. The processing resources 312 may
include one or more application-specific integrated circuits
(`ASICs`) that are customized for some particular purpose as well
as one or more central processing units (`CPUs`). The processing
resources 312 may also include one or more digital signal
processors (`DSPs`), one or more field-programmable gate arrays
(`FPGAs`), one or more systems on a chip (`SoCs`), or other form of
processing resources 312. The storage system 306 may utilize the
storage resources 312 to perform a variety of tasks including, but
not limited to, supporting the execution of software resources 314
that will be described in greater detail below.
The storage system 306 depicted in FIG. 3B also includes software
resources 314 that, when executed by processing resources 312
within the storage system 306, may perform a vast array of tasks.
The software resources 314 may include, for example, one or more
modules of computer program instructions that when executed by
processing resources 312 within the storage system 306 are useful
in carrying out various data protection techniques to preserve the
integrity of data that is stored within the storage systems.
Readers will appreciate that such data protection techniques may be
carried out, for example, by system software executing on computer
hardware within the storage system, by a cloud services provider,
or in other ways. Such data protection techniques can include, for
example, data archiving techniques that cause data that is no
longer actively used to be moved to a separate storage device or
separate storage system for long-term retention, data backup
techniques through which data stored in the storage system may be
copied and stored in a distinct location to avoid data loss in the
event of equipment failure or some other form of catastrophe with
the storage system, data replication techniques through which data
stored in the storage system is replicated to another storage
system such that the data may be accessible via multiple storage
systems, data snapshotting techniques through which the state of
data within the storage system is captured at various points in
time, data and database cloning techniques through which duplicate
copies of data and databases may be created, and other data
protection techniques. Through the use of such data protection
techniques, business continuity and disaster recovery objectives
may be met as a failure of the storage system may not result in the
loss of data stored in the storage system.
The software resources 314 may also include software that is useful
in implementing software-defined storage (`SDS`). In such an
example, the software resources 314 may include one or more modules
of computer program instructions that, when executed, are useful in
policy-based provisioning and management of data storage that is
independent of the underlying hardware. Such software resources 314
may be useful in implementing storage virtualization to separate
the storage hardware from the software that manages the storage
hardware.
The software resources 314 may also include software that is useful
in facilitating and optimizing I/O operations that are directed to
the storage resources 308 in the storage system 306. For example,
the software resources 314 may include software modules that
perform carry out various data reduction techniques such as, for
example, data compression, data deduplication, and others. The
software resources 314 may include software modules that
intelligently group together I/O operations to facilitate better
usage of the underlying storage resource 308, software modules that
perform data migration operations to migrate from within a storage
system, as well as software modules that perform other functions.
Such software resources 314 may be embodied as one or more software
containers or in many other ways.
Readers will appreciate that the presence of such software
resources 314 may provide for an improved user experience of the
storage system 306, an expansion of functionality supported by the
storage system 306, and many other benefits. Consider the specific
example of the software resources 314 carrying out data backup
techniques through which data stored in the storage system may be
copied and stored in a distinct location to avoid data loss in the
event of equipment failure or some other form of catastrophe. In
such an example, the systems described herein may more reliably
(and with less burden placed on the user) perform backup operations
relative to interactive backup management systems that require high
degrees of user interactivity, offer less robust automation and
feature sets, and so on.
For further explanation, FIG. 3C sets forth an example of a
cloud-based storage system 318 in accordance with some embodiments
of the present disclosure. In the example depicted in FIG. 3C, the
cloud-based storage system 318 is created entirely in a cloud
computing environment 316 such as, for example, Amazon Web Services
(`AWS`), Microsoft Azure, Google Cloud Platform, IBM Cloud, Oracle
Cloud, and others. The cloud-based storage system 318 may be used
to provide services similar to the services that may be provided by
the storage systems described above. For example, the cloud-based
storage system 318 may be used to provide block storage services to
users of the cloud-based storage system 318, the cloud-based
storage system 318 may be used to provide storage services to users
of the cloud-based storage system 318 through the use of
solid-state storage, and so on.
The cloud-based storage system 318 depicted in FIG. 3C includes two
cloud computing instances 320, 322 that each are used to support
the execution of a storage controller application 324, 326. The
cloud computing instances 320, 322 may be embodied, for example, as
instances of cloud computing resources (e.g., virtual machines)
that may be provided by the cloud computing environment 316 to
support the execution of software applications such as the storage
controller application 324, 326. In one embodiment, the cloud
computing instances 320, 322 may be embodied as Amazon Elastic
Compute Cloud (`EC2`) instances. In such an example, an Amazon
Machine Image (`AMI`) that includes the storage controller
application 324, 326 may be booted to create and configure a
virtual machine that may execute the storage controller application
324, 326.
In the example method depicted in FIG. 3C, the storage controller
application 324, 326 may be embodied as a module of computer
program instructions that, when executed, carries out various
storage tasks. For example, the storage controller application 324,
326 may be embodied as a module of computer program instructions
that, when executed, carries out the same tasks as the controllers
110A, 110B in FIG. 1A described above such as writing data received
from the users of the cloud-based storage system 318 to the
cloud-based storage system 318, erasing data from the cloud-based
storage system 318, retrieving data from the cloud-based storage
system 318 and providing such data to users of the cloud-based
storage system 318, monitoring and reporting of disk utilization
and performance, performing redundancy operations, such as RAID or
RAID-like data redundancy operations, compressing data, encrypting
data, deduplicating data, and so forth. Readers will appreciate
that because there are two cloud computing instances 320, 322 that
each include the storage controller application 324, 326, in some
embodiments one cloud computing instance 320 may operate as the
primary controller as described above while the other cloud
computing instance 322 may operate as the secondary controller as
described above. In such an example, in order to save costs, the
cloud computing instance 320 that operates as the primary
controller may be deployed on a relatively high-performance and
relatively expensive cloud computing instance while the cloud
computing instance 322 that operates as the secondary controller
may be deployed on a relatively low-performance and relatively
inexpensive cloud computing instance. Readers will appreciate that
the storage controller application 324, 326 depicted in FIG. 3C may
include identical source code that is executed within different
cloud computing instances 320, 322.
Consider an example in which the cloud computing environment 316 is
embodied as AWS and the cloud computing instances are embodied as
EC2 instances. In such an example, AWS offers many types of EC2
instances. For example, AWS offers a suite of general purpose EC2
instances that include varying levels of memory and processing
power. In such an example, the cloud computing instance 320 that
operates as the primary controller may be deployed on one of the
instance types that has a relatively large amount of memory and
processing power while the cloud computing instance 322 that
operates as the secondary controller may be deployed on one of the
instance types that has a relatively small amount of memory and
processing power. In such an example, upon the occurrence of a
failover event where the roles of primary and secondary are
switched, a double failover may actually be carried out such that:
1) a first failover event where the cloud computing instance 322
that formerly operated as the secondary controller begins to
operate as the primary controller, and 2) a third cloud computing
instance (not shown) that is of an instance type that has a
relatively large amount of memory and processing power is spun up
with a copy of the storage controller application, where the third
cloud computing instance begins operating as the primary controller
while the cloud computing instance 322 that originally operated as
the secondary controller begins operating as the secondary
controller again. In such an example, the cloud computing instance
320 that formerly operated as the primary controller may be
terminated. Readers will appreciate that in alternative
embodiments, the cloud computing instance 320 that is operating as
the secondary controller after the failover event may continue to
operate as the secondary controller and the cloud computing
instance 322 that operated as the primary controller after the
occurrence of the failover event may be terminated once the primary
role has been assumed by the third cloud computing instance (not
shown).
Readers will appreciate that while the embodiments described above
relate to embodiments where one cloud computing instance 320
operates as the primary controller and the second cloud computing
instance 322 operates as the secondary controller, other
embodiments are within the scope of the present disclosure. For
example, each cloud computing instance 320, 322 may operate as a
primary controller for some portion of the address space supported
by the cloud-based storage system 318, each cloud computing
instance 320, 322 may operate as a primary controller where the
servicing of I/O operations directed to the cloud-based storage
system 318 are divided in some other way, and so on. In fact, in
other embodiments where costs savings may be prioritized over
performance demands, only a single cloud computing instance may
exist that contains the storage controller application. In such an
example, a controller failure may take more time to recover from as
a new cloud computing instance that includes the storage controller
application would need to be spun up rather than having an already
created cloud computing instance take on the role of servicing I/O
operations that would have otherwise been handled by the failed
cloud computing instance.
The cloud-based storage system 318 depicted in FIG. 3C includes
cloud computing instances 340a, 340b, 340n with local storage 330,
334, 338. The cloud computing instances 340a, 340b, 340n depicted
in FIG. 3C may be embodied, for example, as instances of cloud
computing resources that may be provided by the cloud computing
environment 316 to support the execution of software applications.
The cloud computing instances 340a, 340b, 340n of FIG. 3C may
differ from the cloud computing instances 320, 322 described above
as the cloud computing instances 340a, 340b, 340n of FIG. 3C have
local storage 330, 334, 338 resources whereas the cloud computing
instances 320, 322 that support the execution of the storage
controller application 324, 326 need not have local storage
resources. The cloud computing instances 340a, 340b, 340n with
local storage 330, 334, 338 may be embodied, for example, as EC2 M5
instances that include one or more SSDs, as EC2 R5 instances that
include one or more SSDs, as EC2 I3 instances that include one or
more SSDs, and so on. In some embodiments, the local storage 330,
334, 338 must be embodied as solid-state storage (e.g., SSDs)
rather than storage that makes use of hard disk drives.
In the example depicted in FIG. 3C, each of the cloud computing
instances 340a, 340b, 340n with local storage 330, 334, 338 can
include a software daemon 328, 332, 336 that, when executed by a
cloud computing instance 340a, 340b, 340n can present itself to the
storage controller applications 324, 326 as if the cloud computing
instance 340a, 340b, 340n were a physical storage device (e.g., one
or more SSDs). In such an example, the software daemon 328, 332,
336 may include computer program instructions similar to those that
would normally be contained on a storage device such that the
storage controller applications 324, 326 can send and receive the
same commands that a storage controller would send to storage
devices. In such a way, the storage controller applications 324,
326 may include code that is identical to (or substantially
identical to) the code that would be executed by the controllers in
the storage systems described above. In these and similar
embodiments, communications between the storage controller
applications 324, 326 and the cloud computing instances 340a, 340b,
340n with local storage 330, 334, 338 may utilize iSCSI, NVMe over
TCP, messaging, a custom protocol, or in some other mechanism.
In the example depicted in FIG. 3C, each of the cloud computing
instances 340a, 340b, 340n with local storage 330, 334, 338 may
also be coupled to block-storage 342, 344, 346 that is offered by
the cloud computing environment 316. The block-storage 342, 344,
346 that is offered by the cloud computing environment 316 may be
embodied, for example, as Amazon Elastic Block Store (`EBS`)
volumes. For example, a first EBS volume may be coupled to a first
cloud computing instance 340a, a second EBS volume may be coupled
to a second cloud computing instance 340b, and a third EBS volume
may be coupled to a third cloud computing instance 340n. In such an
example, the block-storage 342, 344, 346 that is offered by the
cloud computing environment 316 may be utilized in a manner that is
similar to how the NVRAM devices described above are utilized, as
the software daemon 328, 332, 336 (or some other module) that is
executing within a particular cloud comping instance 340a, 340b,
340n may, upon receiving a request to write data, initiate a write
of the data to its attached EBS volume as well as a write of the
data to its local storage 330, 334, 338 resources. In some
alternative embodiments, data may only be written to the local
storage 330, 334, 338 resources within a particular cloud comping
instance 340a, 340b, 340n. In an alternative embodiment, rather
than using the block-storage 342, 344, 346 that is offered by the
cloud computing environment 316 as NVRAM, actual RAM on each of the
cloud computing instances 340a, 340b, 340n with local storage 330,
334, 338 may be used as NVRAM, thereby decreasing network
utilization costs that would be associated with using an EBS volume
as the NVRAM.
In the example depicted in FIG. 3C, the cloud computing instances
340a, 340b, 340n with local storage 330, 334, 338 may be utilized,
by cloud computing instances 320, 322 that support the execution of
the storage controller application 324, 326 to service I/O
operations that are directed to the cloud-based storage system 318.
Consider an example in which a first cloud computing instance 320
that is executing the storage controller application 324 is
operating as the primary controller. In such an example, the first
cloud computing instance 320 that is executing the storage
controller application 324 may receive (directly or indirectly via
the secondary controller) requests to write data to the cloud-based
storage system 318 from users of the cloud-based storage system
318. In such an example, the first cloud computing instance 320
that is executing the storage controller application 324 may
perform various tasks such as, for example, deduplicating the data
contained in the request, compressing the data contained in the
request, determining where to the write the data contained in the
request, and so on, before ultimately sending a request to write a
deduplicated, encrypted, or otherwise possibly updated version of
the data to one or more of the cloud computing instances 340a,
340b, 340n with local storage 330, 334, 338. Either cloud computing
instance 320, 322, in some embodiments, may receive a request to
read data from the cloud-based storage system 318 and may
ultimately send a request to read data to one or more of the cloud
computing instances 340a, 340b, 340n with local storage 330, 334,
338.
Readers will appreciate that when a request to write data is
received by a particular cloud computing instance 340a, 340b, 340n
with local storage 330, 334, 338, the software daemon 328, 332, 336
or some other module of computer program instructions that is
executing on the particular cloud computing instance 340a, 340b,
340n may be configured to not only write the data to its own local
storage 330, 334, 338 resources and any appropriate block-storage
342, 344, 346 that are offered by the cloud computing environment
316, but the software daemon 328, 332, 336 or some other module of
computer program instructions that is executing on the particular
cloud computing instance 340a, 340b, 340n may also be configured to
write the data to cloud-based object storage 348 that is attached
to the particular cloud computing instance 340a, 340b, 340n. The
cloud-based object storage 348 that is attached to the particular
cloud computing instance 340a, 340b, 340n may be embodied, for
example, as Amazon Simple Storage Service (`S3`) storage that is
accessible by the particular cloud computing instance 340a, 340b,
340n. In other embodiments, the cloud computing instances 320, 322
that each include the storage controller application 324, 326 may
initiate the storage of the data in the local storage 330, 334, 338
of the cloud computing instances 340a, 340b, 340n and the
cloud-based object storage 348.
Readers will appreciate that, as described above, the cloud-based
storage system 318 may be used to provide block storage services to
users of the cloud-based storage system 318. While the local
storage 330, 334, 338 resources and the block-storage 342, 344, 346
resources that are utilized by the cloud computing instances 340a,
340b, 340n may support block-level access, the cloud-based object
storage 348 that is attached to the particular cloud computing
instance 340a, 340b, 340n supports only object-based access. In
order to address this, the software daemon 328, 332, 336 or some
other module of computer program instructions that is executing on
the particular cloud computing instance 340a, 340b, 340n may be
configured to take blocks of data, package those blocks into
objects, and write the objects to the cloud-based object storage
348 that is attached to the particular cloud computing instance
340a, 340b, 340n.
Consider an example in which data is written to the local storage
330, 334, 338 resources and the block-storage 342, 344, 346
resources that are utilized by the cloud computing instances 340a,
340b, 340n in 1 MB blocks. In such an example, assume that a user
of the cloud-based storage system 318 issues a request to write
data that, after being compressed and deduplicated by the storage
controller application 324, 326 results in the need to write 5 MB
of data. In such an example, writing the data to the local storage
330, 334, 338 resources and the block-storage 342, 344, 346
resources that are utilized by the cloud computing instances 340a,
340b, 340n is relatively straightforward as 5 blocks that are 1 MB
in size are written to the local storage 330, 334, 338 resources
and the block-storage 342, 344, 346 resources that are utilized by
the cloud computing instances 340a, 340b, 340n. In such an example,
the software daemon 328, 332, 336 or some other module of computer
program instructions that is executing on the particular cloud
computing instance 340a, 340b, 340n may be configured to: 1) create
a first object that includes the first 1 MB of data and write the
first object to the cloud-based object storage 348, 2) create a
second object that includes the second 1 MB of data and write the
second object to the cloud-based object storage 348, 3) create a
third object that includes the third 1 MB of data and write the
third object to the cloud-based object storage 348, and so on. As
such, in some embodiments, each object that is written to the
cloud-based object storage 348 may be identical (or nearly
identical) in size. Readers will appreciate that in such an
example, metadata that is associated with the data itself may be
included in each object (e.g., the first 1 MB of the object is data
and the remaining portion is metadata associated with the
data).
Readers will appreciate that the cloud-based object storage 348 may
be incorporated into the cloud-based storage system 318 to increase
the durability of the cloud-based storage system 318. Continuing
with the example described above where the cloud computing
instances 340a, 340b, 340n are EC2 instances, readers will
understand that EC2 instances are only guaranteed to have a monthly
uptime of 99.9% and data stored in the local instance store only
persists during the lifetime of the EC2 instance. As such, relying
on the cloud computing instances 340a, 340b, 340n with local
storage 330, 334, 338 as the only source of persistent data storage
in the cloud-based storage system 318 may result in a relatively
unreliable storage system. Likewise, EBS volumes are designed for
99.999% availability. As such, even relying on EBS as the
persistent data store in the cloud-based storage system 318 may
result in a storage system that is not sufficiently durable. Amazon
S3, however, is designed to provide 99.999999999% durability,
meaning that a cloud-based storage system 318 that can incorporate
S3 into its pool of storage is substantially more durable than
various other options.
Readers will appreciate that while a cloud-based storage system 318
that can incorporate S3 into its pool of storage is substantially
more durable than various other options, utilizing S3 as the
primary pool of storage may result in storage system that has
relatively slow response times and relatively long I/O latencies.
As such, the cloud-based storage system 318 depicted in FIG. 3C not
only stores data in S3 but the cloud-based storage system 318 also
stores data in local storage 330, 334, 338 resources and
block-storage 342, 344, 346 resources that are utilized by the
cloud computing instances 340a, 340b, 340n, such that read
operations can be serviced from local storage 330, 334, 338
resources and the block-storage 342, 344, 346 resources that are
utilized by the cloud computing instances 340a, 340b, 340n, thereby
reducing read latency when users of the cloud-based storage system
318 attempt to read data from the cloud-based storage system
318.
In some embodiments, all data that is stored by the cloud-based
storage system 318 may be stored in both: 1) the cloud-based object
storage 348, and 2) at least one of the local storage 330, 334, 338
resources or block-storage 342, 344, 346 resources that are
utilized by the cloud computing instances 340a, 340b, 340n. In such
embodiments, the local storage 330, 334, 338 resources and
block-storage 342, 344, 346 resources that are utilized by the
cloud computing instances 340a, 340b, 340n may effectively operate
as cache that generally includes all data that is also stored in
S3, such that all reads of data may be serviced by the cloud
computing instances 340a, 340b, 340n without requiring the cloud
computing instances 340a, 340b, 340n to access the cloud-based
object storage 348. Readers will appreciate that in other
embodiments, however, all data that is stored by the cloud-based
storage system 318 may be stored in the cloud-based object storage
348, but less than all data that is stored by the cloud-based
storage system 318 may be stored in at least one of the local
storage 330, 334, 338 resources or block-storage 342, 344, 346
resources that are utilized by the cloud computing instances 340a,
340b, 340n. In such an example, various policies may be utilized to
determine which subset of the data that is stored by the
cloud-based storage system 318 should reside in both: 1) the
cloud-based object storage 348, and 2) at least one of the local
storage 330, 334, 338 resources or block-storage 342, 344, 346
resources that are utilized by the cloud computing instances 340a,
340b, 340n.
As described above, when the cloud computing instances 340a, 340b,
340n with local storage 330, 334, 338 are embodied as EC2
instances, the cloud computing instances 340a, 340b, 340n with
local storage 330, 334, 338 are only guaranteed to have a monthly
uptime of 99.9% and data stored in the local instance store only
persists during the lifetime of each cloud computing instance 340a,
340b, 340n with local storage 330, 334, 338. As such, one or more
modules of computer program instructions that are executing within
the cloud-based storage system 318 (e.g., a monitoring module that
is executing on its own EC2 instance) may be designed to handle the
failure of one or more of the cloud computing instances 340a, 340b,
340n with local storage 330, 334, 338. In such an example, the
monitoring module may handle the failure of one or more of the
cloud computing instances 340a, 340b, 340n with local storage 330,
334, 338 by creating one or more new cloud computing instances with
local storage, retrieving data that was stored on the failed cloud
computing instances 340a, 340b, 340n from the cloud-based object
storage 348, and storing the data retrieved from the cloud-based
object storage 348 in local storage on the newly created cloud
computing instances. Readers will appreciate that many variants of
this process may be implemented.
Consider an example in which all cloud computing instances 340a,
340b, 340n with local storage 330, 334, 338 failed. In such an
example, the monitoring module may create new cloud computing
instances with local storage, where high-bandwidth instances types
are selected that allow for the maximum data transfer rates between
the newly created high-bandwidth cloud computing instances with
local storage and the cloud-based object storage 348. Readers will
appreciate that instances types are selected that allow for the
maximum data transfer rates between the new cloud computing
instances and the cloud-based object storage 348 such that the new
high-bandwidth cloud computing instances can be rehydrated with
data from the cloud-based object storage 348 as quickly as
possible. Once the new high-bandwidth cloud computing instances are
rehydrated with data from the cloud-based object storage 348, less
expensive lower-bandwidth cloud computing instances may be created,
data may be migrated to the less expensive lower-bandwidth cloud
computing instances, and the high-bandwidth cloud computing
instances may be terminated.
Readers will appreciate that in some embodiments, the number of new
cloud computing instances that are created may substantially exceed
the number of cloud computing instances that are needed to locally
store all of the data stored by the cloud-based storage system 318.
The number of new cloud computing instances that are created may
substantially exceed the number of cloud computing instances that
are needed to locally store all of the data stored by the
cloud-based storage system 318 in order to more rapidly pull data
from the cloud-based object storage 348 and into the new cloud
computing instances, as each new cloud computing instance can (in
parallel) retrieve some portion of the data stored by the
cloud-based storage system 318. In such embodiments, once the data
stored by the cloud-based storage system 318 has been pulled into
the newly created cloud computing instances, the data may be
consolidated within a subset of the newly created cloud computing
instances and those newly created cloud computing instances that
are excessive may be terminated.
Consider an example in which 1000 cloud computing instances are
needed in order to locally store all valid data that users of the
cloud-based storage system 318 have written to the cloud-based
storage system 318. In such an example, assume that all 1,000 cloud
computing instances fail. In such an example, the monitoring module
may cause 100,000 cloud computing instances to be created, where
each cloud computing instance is responsible for retrieving, from
the cloud-based object storage 348, distinct 1/100,000th chunks of
the valid data that users of the cloud-based storage system 318
have written to the cloud-based storage system 318 and locally
storing the distinct chunk of the dataset that it retrieved. In
such an example, because each of the 100,000 cloud computing
instances can retrieve data from the cloud-based object storage 348
in parallel, the caching layer may be restored 100 times faster as
compared to an embodiment where the monitoring module only create
1000 replacement cloud computing instances. In such an example,
over time the data that is stored locally in the 100,000 could be
consolidated into 1,000 cloud computing instances and the remaining
99,000 cloud computing instances could be terminated.
Readers will appreciate that various performance aspects of the
cloud-based storage system 318 may be monitored (e.g., by a
monitoring module that is executing in an EC2 instance) such that
the cloud-based storage system 318 can be scaled-up or scaled-out
as needed. Consider an example in which the monitoring module
monitors the performance of the could-based storage system 318 via
communications with one or more of the cloud computing instances
320, 322 that each are used to support the execution of a storage
controller application 324, 326, via monitoring communications
between cloud computing instances 320, 322, 340a, 340b, 340n, via
monitoring communications between cloud computing instances 320,
322, 340a, 340b, 340n and the cloud-based object storage 348, or in
some other way. In such an example, assume that the monitoring
module determines that the cloud computing instances 320, 322 that
are used to support the execution of a storage controller
application 324, 326 are undersized and not sufficiently servicing
the I/O requests that are issued by users of the cloud-based
storage system 318. In such an example, the monitoring module may
create a new, more powerful cloud computing instance (e.g., a cloud
computing instance of a type that includes more processing power,
more memory, etc . . . ) that includes the storage controller
application such that the new, more powerful cloud computing
instance can begin operating as the primary controller. Likewise,
if the monitoring module determines that the cloud computing
instances 320, 322 that are used to support the execution of a
storage controller application 324, 326 are oversized and that cost
savings could be gained by switching to a smaller, less powerful
cloud computing instance, the monitoring module may create a new,
less powerful (and less expensive) cloud computing instance that
includes the storage controller application such that the new, less
powerful cloud computing instance can begin operating as the
primary controller.
Consider, as an additional example of dynamically sizing the
cloud-based storage system 318, an example in which the monitoring
module determines that the utilization of the local storage that is
collectively provided by the cloud computing instances 340a, 340b,
340n has reached a predetermined utilization threshold (e.g., 95%).
In such an example, the monitoring module may create additional
cloud computing instances with local storage to expand the pool of
local storage that is offered by the cloud computing instances.
Alternatively, the monitoring module may create one or more new
cloud computing instances that have larger amounts of local storage
than the already existing cloud computing instances 340a, 340b,
340n, such that data stored in an already existing cloud computing
instance 340a, 340b, 340n can be migrated to the one or more new
cloud computing instances and the already existing cloud computing
instance 340a, 340b, 340n can be terminated, thereby expanding the
pool of local storage that is offered by the cloud computing
instances. Likewise, if the pool of local storage that is offered
by the cloud computing instances is unnecessarily large, data can
be consolidated and some cloud computing instances can be
terminated.
Readers will appreciate that the cloud-based storage system 318 may
be sized up and down automatically by a monitoring module applying
a predetermined set of rules that may be relatively simple of
relatively complicated. In fact, the monitoring module may not only
take into account the current state of the cloud-based storage
system 318, but the monitoring module may also apply predictive
policies that are based on, for example, observed behavior (e.g.,
every night from 10 PM until 6 AM usage of the storage system is
relatively light), predetermined fingerprints (e.g., every time a
virtual desktop infrastructure adds 100 virtual desktops, the
number of IOPS directed to the storage system increase by X), and
so on. In such an example, the dynamic scaling of the cloud-based
storage system 318 may be based on current performance metrics,
predicted workloads, and many other factors, including combinations
thereof.
Readers will further appreciate that because the cloud-based
storage system 318 may be dynamically scaled, the cloud-based
storage system 318 may even operate in a way that is more dynamic.
Consider the example of garbage collection. In a traditional
storage system, the amount of storage is fixed. As such, at some
point the storage system may be forced to perform garbage
collection as the amount of available storage has become so
constrained that the storage system is on the verge of running out
of storage. In contrast, the cloud-based storage system 318
described here can always `add` additional storage (e.g., by adding
more cloud computing instances with local storage). Because the
cloud-based storage system 318 described here can always `add`
additional storage, the cloud-based storage system 318 can make
more intelligent decisions regarding when to perform garbage
collection. For example, the cloud-based storage system 318 may
implement a policy that garbage collection only be performed when
the number of IOPS being serviced by the cloud-based storage system
318 falls below a certain level. In some embodiments, other
system-level functions (e.g., deduplication, compression) may also
be turned off and on in response to system load, given that the
size of the cloud-based storage system 318 is not constrained in
the same way that traditional storage systems are constrained.
Readers will appreciate that embodiments of the present disclosure
resolve an issue with block-storage services offered by some cloud
computing environments as some cloud computing environments only
allow for one cloud computing instance to connect to a
block-storage volume at a single time. For example, in Amazon AWS,
only a single EC2 instance may be connected to an EBS volume.
Through the use of EC2 instances with local storage, embodiments of
the present disclosure can offer multi-connect capabilities where
multiple EC2 instances can connect to another EC2 instance with
local storage (`a drive instance`). In such embodiments, the drive
instances may include software executing within the drive instance
that allows the drive instance to support I/O directed to a
particular volume from each connected EC2 instance. As such, some
embodiments of the present disclosure may be embodied as
multi-connect block storage services that may not include all of
the components depicted in FIG. 3C.
In some embodiments, especially in embodiments where the
cloud-based object storage 348 resources are embodied as Amazon S3,
the cloud-based storage system 318 may include one or more modules
(e.g., a module of computer program instructions executing on an
EC2 instance) that are configured to ensure that when the local
storage of a particular cloud computing instance is rehydrated with
data from S3, the appropriate data is actually in S3. This issue
arises largely because S3 implements an eventual consistency model
where, when overwriting an existing object, reads of the object
will eventually (but not necessarily immediately) become consistent
and will eventually (but not necessarily immediately) return the
overwritten version of the object. To address this issue, in some
embodiments of the present disclosure, objects in S3 are never
overwritten. Instead, a traditional `overwrite` would result in the
creation of the new object (that includes the updated version of
the data) and the eventual deletion of the old object (that
includes the previous version of the data).
In some embodiments of the present disclosure, as part of an
attempt to never (or almost never) overwrite an object, when data
is written to S3 the resultant object may be tagged with a sequence
number. In some embodiments, these sequence numbers may be
persisted elsewhere (e.g., in a database) such that at any point in
time, the sequence number associated with the most up-to-date
version of some piece of data can be known. In such a way, a
determination can be made as to whether S3 has the most recent
version of some piece of data by merely reading the sequence number
associated with an object--and without actually reading the data
from S3. The ability to make this determination may be particularly
important when a cloud computing instance with local storage
crashes, as it would be undesirable to rehydrate the local storage
of a replacement cloud computing instance with out-of-date data. In
fact, because the cloud-based storage system 318 does not need to
access the data to verify its validity, the data can stay encrypted
and access charges can be avoided.
The storage systems described above may carry out intelligent data
backup techniques through which data stored in the storage system
may be copied and stored in a distinct location to avoid data loss
in the event of equipment failure or some other form of
catastrophe. For example, the storage systems described above may
be configured to examine each backup to avoid restoring the storage
system to an undesirable state. Consider an example in which
malware infects the storage system. In such an example, the storage
system may include software resources 314 that can scan each backup
to identify backups that were captured before the malware infected
the storage system and those backups that were captured after the
malware infected the storage system. In such an example, the
storage system may restore itself from a backup that does not
include the malware--or at least not restore the portions of a
backup that contained the malware. In such an example, the storage
system may include software resources 314 that can scan each backup
to identify the presences of malware (or a virus, or some other
undesirable), for example, by identifying write operations that
were serviced by the storage system and originated from a network
subnet that is suspected to have delivered the malware, by
identifying write operations that were serviced by the storage
system and originated from a user that is suspected to have
delivered the malware, by identifying write operations that were
serviced by the storage system and examining the content of the
write operation against fingerprints of the malware, and in many
other ways.
Readers will further appreciate that the backups (often in the form
of one or more snapshots) may also be utilized to perform rapid
recovery of the storage system. Consider an example in which the
storage system is infected with ransomware that locks users out of
the storage system. In such an example, software resources 314
within the storage system may be configured to detect the presence
of ransomware and may be further configured to restore the storage
system to a point-in-time, using the retained backups, prior to the
point-in-time at which the ransomware infected the storage system.
In such an example, the presence of ransomware may be explicitly
detected through the use of software tools utilized by the system,
through the use of a key (e.g., a USB drive) that is inserted into
the storage system, or in a similar way. Likewise, the presence of
ransomware may be inferred in response to system activity meeting a
predetermined fingerprint such as, for example, no reads or writes
coming into the system for a predetermined period of time.
Readers will appreciate that the various components depicted in
FIG. 3B may be grouped into one or more optimized computing
packages as converged infrastructures. Such converged
infrastructures may include pools of computers, storage and
networking resources that can be shared by multiple applications
and managed in a collective manner using policy-driven processes.
Such converged infrastructures may minimize compatibility issues
between various components within the storage system 306 while also
reducing various costs associated with the establishment and
operation of the storage system 306. Such converged infrastructures
may be implemented with a converged infrastructure reference
architecture, with standalone appliances, with a software driven
hyper-converged approach (e.g., hyper-converged infrastructures),
or in other ways.
Readers will appreciate that the storage system 306 depicted in
FIG. 3B may be useful for supporting various types of software
applications. For example, the storage system 306 may be useful in
supporting artificial intelligence (`AI`) applications, database
applications, DevOps projects, electronic design automation tools,
event-driven software applications, high performance computing
applications, simulation applications, high-speed data capture and
analysis applications, machine learning applications, media
production applications, media serving applications, picture
archiving and communication systems (`PACS`) applications, software
development applications, virtual reality applications, augmented
reality applications, and many other types of applications by
providing storage resources to such applications.
The storage systems described above may operate to support a wide
variety of applications. In view of the fact that the storage
systems include compute resources, storage resources, and a wide
variety of other resources, the storage systems may be well suited
to support applications that are resource intensive such as, for
example, AI applications. Such AI applications may enable devices
to perceive their environment and take actions that maximize their
chance of success at some goal. Examples of such AI applications
can include IBM Watson, Microsoft Oxford, Google DeepMind, Baidu
Minwa, and others. The storage systems described above may also be
well suited to support other types of applications that are
resource intensive such as, for example, machine learning
applications. Machine learning applications may perform various
types of data analysis to automate analytical model building. Using
algorithms that iteratively learn from data, machine learning
applications can enable computers to learn without being explicitly
programmed. One particular area of machine learning is referred to
as reinforcement learning, which involves taking suitable actions
to maximize reward in a particular situation. Reinforcement
learning may be employed to find the best possible behavior or path
that a particular software application or machine should take in a
specific situation. Reinforcement learning differs from other areas
of machine learning (e.g., supervised learning, unsupervised
learning) in that correct input/output pairs need not be presented
for reinforcement learning and sub-optimal actions need not be
explicitly corrected.
In addition to the resources already described, the storage systems
described above may also include graphics processing units
(`GPUs`), occasionally referred to as visual processing unit
(`VPUs`). Such GPUs may be embodied as specialized electronic
circuits that rapidly manipulate and alter memory to accelerate the
creation of images in a frame buffer intended for output to a
display device. Such GPUs may be included within any of the
computing devices that are part of the storage systems described
above, including as one of many individually scalable components of
a storage system, where other examples of individually scalable
components of such storage system can include storage components,
memory components, compute components (e.g., CPUs, FPGAs, ASICs),
networking components, software components, and others. In addition
to GPUs, the storage systems described above may also include
neural network processors (`NNPs`) for use in various aspects of
neural network processing. Such NNPs may be used in place of (or in
addition to) GPUs and may be also be independently scalable.
As described above, the storage systems described herein may be
configured to support artificial intelligence applications, machine
learning applications, big data analytics applications, and many
other types of applications. The rapid growth in these sort of
applications is being driven by three technologies: deep learning
(DL), GPU processors, and Big Data. Deep learning is a computing
model that makes use of massively parallel neural networks inspired
by the human brain. Instead of experts handcrafting software, a
deep learning model writes its own software by learning from lots
of examples. A GPU is a modern processor with thousands of cores,
well-suited to run algorithms that loosely represent the parallel
nature of the human brain.
Advances in deep neural networks have ignited a new wave of
algorithms and tools for data scientists to tap into their data
with artificial intelligence (AI). With improved algorithms, larger
data sets, and various frameworks (including open-source software
libraries for machine learning across a range of tasks), data
scientists are tackling new use cases like autonomous driving
vehicles, natural language processing and understanding, computer
vision, machine reasoning, strong AI, and many others. Applications
of such techniques may include: machine and vehicular object
detection, identification and avoidance; visual recognition,
classification and tagging; algorithmic financial trading strategy
performance management; simultaneous localization and mapping;
predictive maintenance of high-value machinery; prevention against
cyber security threats, expertise automation; image recognition and
classification; question answering; robotics; text analytics
(extraction, classification) and text generation and translation;
and many others. Applications of AI techniques has materialized in
a wide array of products include, for example, Amazon Echo's speech
recognition technology that allows users to talk to their machines,
Google Translate.TM. which allows for machine-based language
translation, Spotify's Discover Weekly that provides
recommendations on new songs and artists that a user may like based
on the user's usage and traffic analysis, Quill's text generation
offering that takes structured data and turns it into narrative
stories, Chatbots that provide real-time, contextually specific
answers to questions in a dialog format, and many others.
Furthermore, AI may impact a wide variety of industries and
sectors. For example, AI solutions may be used in healthcare to
take clinical notes, patient files, research data, and other inputs
to generate potential treatment options for doctors to explore.
Likewise, AI solutions may be used by retailers to personalize
consumer recommendations based on a person's digital footprint of
behaviors, profile data, or other data.
Training deep neural networks, however, requires both high quality
input data and large amounts of computation. GPUs are massively
parallel processors capable of operating on large amounts of data
simultaneously. When combined into a multi-GPU cluster, a high
throughput pipeline may be required to feed input data from storage
to the compute engines. Deep learning is more than just
constructing and training models. There also exists an entire data
pipeline that must be designed for the scale, iteration, and
experimentation necessary for a data science team to succeed.
Data is the heart of modern AI and deep learning algorithms. Before
training can begin, one problem that must be addressed revolves
around collecting the labeled data that is crucial for training an
accurate AI model. A full scale AI deployment may be required to
continuously collect, clean, transform, label, and store large
amounts of data. Adding additional high quality data points
directly translates to more accurate models and better insights.
Data samples may undergo a series of processing steps including,
but not limited to: 1) ingesting the data from an external source
into the training system and storing the data in raw form, 2)
cleaning and transforming the data in a format convenient for
training, including linking data samples to the appropriate label,
3) exploring parameters and models, quickly testing with a smaller
dataset, and iterating to converge on the most promising models to
push into the production cluster, 4) executing training phases to
select random batches of input data, including both new and older
samples, and feeding those into production GPU servers for
computation to update model parameters, and 5) evaluating including
using a holdback portion of the data not used in training in order
to evaluate model accuracy on the holdout data. This lifecycle may
apply for any type of parallelized machine learning, not just
neural networks or deep learning. For example, standard machine
learning frameworks may rely on CPUs instead of GPUs but the data
ingest and training workflows may be the same. Readers will
appreciate that a single shared storage data hub creates a
coordination point throughout the lifecycle without the need for
extra data copies among the ingest, preprocessing, and training
stages. Rarely is the ingested data used for only one purpose, and
shared storage gives the flexibility to train multiple different
models or apply traditional analytics to the data.
Readers will appreciate that each stage in the AI data pipeline may
have varying requirements from the data hub (e.g., the storage
system or collection of storage systems). Scale-out storage systems
must deliver uncompromising performance for all manner of access
types and patterns--from small, metadata-heavy to large files, from
random to sequential access patterns, and from low to high
concurrency. The storage systems described above may serve as an
ideal AI data hub as the systems may service unstructured
workloads. In the first stage, data is ideally ingested and stored
on to the same data hub that following stages will use, in order to
avoid excess data copying. The next two steps can be done on a
standard compute server that optionally includes a GPU, and then in
the fourth and last stage, full training production jobs are run on
powerful GPU-accelerated servers. Often, there is a production
pipeline alongside an experimental pipeline operating on the same
dataset. Further, the GPU-accelerated servers can be used
independently for different models or joined together to train on
one larger model, even spanning multiple systems for distributed
training. If the shared storage tier is slow, then data must be
copied to local storage for each phase, resulting in wasted time
staging data onto different servers. The ideal data hub for the AI
training pipeline delivers performance similar to data stored
locally on the server node while also having the simplicity and
performance to enable all pipeline stages to operate
concurrently.
A data scientist works to improve the usefulness of the trained
model through a wide variety of approaches: more data, better data,
smarter training, and deeper models. In many cases, there will be
teams of data scientists sharing the same datasets and working in
parallel to produce new and improved training models. Often, there
is a team of data scientists working within these phases
concurrently on the same shared datasets. Multiple, concurrent
workloads of data processing, experimentation, and full-scale
training layer the demands of multiple access patterns on the
storage tier. In other words, storage cannot just satisfy large
file reads, but must contend with a mix of large and small file
reads and writes. Finally, with multiple data scientists exploring
datasets and models, it may be critical to store data in its native
format to provide flexibility for each user to transform, clean,
and use the data in a unique way. The storage systems described
above may provide a natural shared storage home for the dataset,
with data protection redundancy (e.g., by using RAID6) and the
performance necessary to be a common access point for multiple
developers and multiple experiments. Using the storage systems
described above may avoid the need to carefully copy subsets of the
data for local work, saving both engineering and GPU-accelerated
servers use time. These copies become a constant and growing tax as
the raw data set and desired transformations constantly update and
change.
Readers will appreciate that a fundamental reason why deep learning
has seen a surge in success is the continued improvement of models
with larger data set sizes. In contrast, classical machine learning
algorithms, like logistic regression, stop improving in accuracy at
smaller data set sizes. As such, the separation of compute
resources and storage resources may also allow independent scaling
of each tier, avoiding many of the complexities inherent in
managing both together. As the data set size grows or new data sets
are considered, a scale out storage system must be able to expand
easily. Similarly, if more concurrent training is required,
additional GPUs or other compute resources can be added without
concern for their internal storage.
Small file performance of the storage tier may be critical as many
types of inputs, including text, audio, or images will be natively
stored as small files. If the storage tier does not handle small
files well, an extra step will be required to pre-process and group
samples into larger files. Storage, built on top of spinning disks,
that relies on SSD as a caching tier, may fall short of the
performance needed. Because training with random input batches
results in more accurate models, the entire data set must be
accessible with full performance. SSD caches only provide high
performance for a small subset of the data and will be ineffective
at hiding the latency of spinning drives.
Although the preceding paragraphs discuss deep learning
applications, readers will appreciate that the storage systems
described herein may also be part of a distributed deep learning
(`DDL`) platform to support the execution of DDL algorithms. The
storage systems described above may also be paired with other
technologies such as TensorFlow, an open-source software library
for dataflow programming across a range of tasks that may be used
for machine learning applications such as neural networks, to
facilitate the development of such machine learning models,
applications, and so on.
The storage systems described above may also be used in a
neuromorphic computing environment. Neuromorphic computing is a
form of computing that mimics brain cells. To support neuromorphic
computing, an architecture of interconnected "neurons" replace
traditional computing models with low-powered signals that go
directly between neurons for more efficient computation.
Neuromorphic computing may make use of very-large-scale integration
(VLSI) systems containing electronic analog circuits to mimic
neuro-biological architectures present in the nervous system, as
well as analog, digital, mixed-mode analog/digital VLSI, and
software systems that implement models of neural systems for
perception, motor control, or multisensory integration.
Readers will appreciate that the storage systems described above
may be configured to support the storage or use of (among other
types of data) blockchains. In addition to supporting the storage
and use of blockchain technologies, the storage systems described
above may also support the storage and use of derivative items such
as, for example, open source blockchains and related tools that are
part of the IBM.TM. Hyperledger project, permissioned blockchains
in which a certain number of trusted parties are allowed to access
the block chain, blockchain products that enable developers to
build their own distributed ledger projects, and others.
Blockchains and the storage systems described herein may be
leveraged to support on-chain storage of data as well as off-chain
storage of data.
Off-chain storage of data can be implemented in a variety of ways
and can occur when the data itself is not stored within the
blockchain. For example, in one embodiment, a hash function may be
utilized and the data itself may be fed into the hash function to
generate a hash value. In such an example, the hashes of large
pieces of data may be embedded within transactions, instead of the
data itself. Readers will appreciate that, in other embodiments,
alternatives to blockchains may be used to facilitate the
decentralized storage of information. For example, one alternative
to a blockchain that may be used is a blockweave. While
conventional blockchains store every transaction to achieve
validation, a blockweave permits secure decentralization without
the usage of the entire chain, thereby enabling low cost on-chain
storage of data. Such blockweaves may utilize a consensus mechanism
that is based on proof of access (PoA) and proof of work (PoW).
While typical PoW systems only depend on the previous block in
order to generate each successive block, the PoA algorithm may
incorporate data from a randomly chosen previous block. Combined
with the blockweave data structure, miners do not need to store all
blocks (forming a blockchain), but rather can store any previous
blocks forming a weave of blocks (a blockweave). This enables
increased levels of scalability, speed and low-cost and reduces the
cost of data storage in part because miners need not store all
blocks, thereby resulting in a substantial reduction in the amount
of electricity that is consumed during the mining process because,
as the network expands, electricity consumption decreases because a
blockweave demands less and less hashing power for consensus as
data is added to the system. Furthermore, blockweaves may be
deployed on a decentralized storage network in which incentives are
created to encourage rapid data sharing. Such decentralized storage
networks may also make use of blockshadowing techniques, where
nodes only send a minimal block "shadow" to other nodes that allows
peers to reconstruct a full block, instead of transmitting the full
block itself.
The storage systems described above may, either alone or in
combination with other computing devices, be used to support
in-memory computing applications. In-memory computing involves the
storage of information in RAM that is distributed across a cluster
of computers. Readers will appreciate that the storage systems
described above, especially those that are configurable with
customizable amounts of processing resources, storage resources,
and memory resources (e.g., those systems in which blades that
contain configurable amounts of each type of resource), may be
configured in a way so as to provide an infrastructure that can
support in-memory computing. Likewise, the storage systems
described above may include component parts (e.g., NVDIMMs, 3D
crosspoint storage that provide fast random access memory that is
persistent) that can actually provide for an improved in-memory
computing environment as compared to in-memory computing
environments that rely on RAM distributed across dedicated
servers.
In some embodiments, the storage systems described above may be
configured to operate as a hybrid in-memory computing environment
that includes a universal interface to all storage media (e.g.,
RAM, flash storage, 3D crosspoint storage). In such embodiments,
users may have no knowledge regarding the details of where their
data is stored but they can still use the same full, unified API to
address data. In such embodiments, the storage system may (in the
background) move data to the fastest layer available--including
intelligently placing the data in dependence upon various
characteristics of the data or in dependence upon some other
heuristic. In such an example, the storage systems may even make
use of existing products such as Apache Ignite and GridGain to move
data between the various storage layers, or the storage systems may
make use of custom software to move data between the various
storage layers. The storage systems described herein may implement
various optimizations to improve the performance of in-memory
computing such as, for example, having computations occur as close
to the data as possible.
Readers will further appreciate that in some embodiments, the
storage systems described above may be paired with other resources
to support the applications described above. For example, one
infrastructure could include primary compute in the form of servers
and workstations which specialize in using General-purpose
computing on graphics processing units (`GPGPU`) to accelerate deep
learning applications that are interconnected into a computation
engine to train parameters for deep neural networks. Each system
may have Ethernet external connectivity, InfiniBand external
connectivity, some other form of external connectivity, or some
combination thereof. In such an example, the GPUs can be grouped
for a single large training or used independently to train multiple
models. The infrastructure could also include a storage system such
as those described above to provide, for example, a scale-out
all-flash file or object store through which data can be accessed
via high-performance protocols such as NFS, S3, and so on. The
infrastructure can also include, for example, redundant top-of-rack
Ethernet switches connected to storage and compute via ports in
MLAG port channels for redundancy. The infrastructure could also
include additional compute in the form of whitebox servers,
optionally with GPUs, for data ingestion, pre-processing, and model
debugging. Readers will appreciate that additional infrastructures
are also be possible.
Readers will appreciate that the systems described above may be
better suited for the applications described above relative to
other systems that may include, for example, a distributed
direct-attached storage (DDAS) solution deployed in server nodes.
Such DDAS solutions may be built for handling large, less
sequential accesses but may be less able to handle small, random
accesses. Readers will further appreciate that the storage systems
described above may be utilized to provide a platform for the
applications described above that is preferable to the utilization
of cloud-based resources as the storage systems may be included in
an on-site or in-house infrastructure that is more secure, more
locally and internally managed, more robust in feature sets and
performance, or otherwise preferable to the utilization of
cloud-based resources as part of a platform to support the
applications described above. For example, services built on
platforms such as IBM's Watson may require a business enterprise to
distribute individual user information, such as financial
transaction information or identifiable patient records, to other
institutions. As such, cloud-based offerings of AI as a service may
be less desirable than internally managed and offered AI as a
service that is supported by storage systems such as the storage
systems described above, for a wide array of technical reasons as
well as for various business reasons.
Readers will appreciate that the storage systems described above,
either alone or in coordination with other computing machinery may
be configured to support other AI related tools. For example, the
storage systems may make use of tools like ONXX or other open
neural network exchange formats that make it easier to transfer
models written in different AI frameworks. Likewise, the storage
systems may be configured to support tools like Amazon's Gluon that
allow developers to prototype, build, and train deep learning
models. In fact, the storage systems described above may be part of
a larger platform, such as IBM.TM. Cloud Private for Data, that
includes integrated data science, data engineering and application
building services. Such platforms may seamlessly collect, organize,
secure, and analyze data across an enterprise, as well as simplify
hybrid data management, unified data governance and integration,
data science and business analytics with a single solution.
Readers will further appreciate that the storage systems described
above may also be deployed as an edge solution. Such an edge
solution may be in place to optimize cloud computing systems by
performing data processing at the edge of the network, near the
source of the data. Edge computing can push applications, data and
computing power (i.e., services) away from centralized points to
the logical extremes of a network. Through the use of edge
solutions such as the storage systems described above,
computational tasks may be performed using the compute resources
provided by such storage systems, data may be storage using the
storage resources of the storage system, and cloud-based services
may be accessed through the use of various resources of the storage
system (including networking resources). By performing
computational tasks on the edge solution, storing data on the edge
solution, and generally making use of the edge solution, the
consumption of expensive cloud-based resources may be avoided and,
in fact, performance improvements may be experienced relative to a
heavier reliance on cloud-based resources.
While many tasks may benefit from the utilization of an edge
solution, some particular uses may be especially suited for
deployment in such an environment. For example, devices like
drones, autonomous cars, robots, and others may require extremely
rapid processing--so fast, in fact, that sending data up to a cloud
environment and back to receive data processing support may simply
be too slow. Likewise, machines like locomotives and gas turbines
that generate large amounts of information through the use of a
wide array of data-generating sensors may benefit from the rapid
data processing capabilities of an edge solution. As an additional
example, some IoT devices such as connected video cameras may not
be well-suited for the utilization of cloud-based resources as it
may be impractical (not only from a privacy perspective, security
perspective, or a financial perspective) to send the data to the
cloud simply because of the pure volume of data that is involved.
As such, many tasks that really on data processing, storage, or
communications may be better suited by platforms that include edge
solutions such as the storage systems described above.
The storage systems described above may alone, or in combination
with other computing resources, serves as a network edge platform
that combines compute resources, storage resources, networking
resources, cloud technologies and network virtualization
technologies, and so on. As part of the network, the edge may take
on characteristics similar to other network facilities, from the
customer premise and backhaul aggregation facilities to Points of
Presence (PoPs) and regional data centers. Readers will appreciate
that network workloads, such as Virtual Network Functions (VNFs)
and others, will reside on the network edge platform. Enabled by a
combination of containers and virtual machines, the network edge
platform may rely on controllers and schedulers that are no longer
geographically co-located with the data processing resources. The
functions, as microservices, may split into control planes, user
and data planes, or even state machines, allowing for independent
optimization and scaling techniques to be applied. Such user and
data planes may be enabled through increased accelerators, both
those residing in server platforms, such as FPGAs and Smart NICs,
and through SDN-enabled merchant silicon and programmable
ASICs.
The storage systems described above may also be optimized for use
in big data analytics. Big data analytics may be generally
described as the process of examining large and varied data sets to
uncover hidden patterns, unknown correlations, market trends,
customer preferences and other useful information that can help
organizations make more-informed business decisions. Big data
analytics applications enable data scientists, predictive modelers,
statisticians and other analytics professionals to analyze growing
volumes of structured transaction data, plus other forms of data
that are often left untapped by conventional business intelligence
(BI) and analytics programs. As part of that process,
semi-structured and unstructured data such as, for example,
internet clickstream data, web server logs, social media content,
text from customer emails and survey responses, mobile-phone
call-detail records, IoT sensor data, and other data may be
converted to a structured form. Big data analytics is a form of
advanced analytics, which involves complex applications with
elements such as predictive models, statistical algorithms and
what-if analyses powered by high-performance analytics systems.
The storage systems described above may also support (including
implementing as a system interface) applications that perform tasks
in response to human speech. For example, the storage systems may
support the execution intelligent personal assistant applications
such as, for example, Amazon's Alexa, Apple Siri, Google Voice,
Samsung Bixby, Microsoft Cortana, and others. While the examples
described in the previous sentence make use of voice as input, the
storage systems described above may also support chatbots,
talkbots, chatterbots, or artificial conversational entities or
other applications that are configured to conduct a conversation
via auditory or textual methods. Likewise, the storage system may
actually execute such an application to enable a user such as a
system administrator to interact with the storage system via
speech. Such applications are generally capable of voice
interaction, music playback, making to-do lists, setting alarms,
streaming podcasts, playing audiobooks, and providing weather,
traffic, and other real time information, such as news, although in
embodiments in accordance with the present disclosure, such
applications may be utilized as interfaces to various system
management operations.
The storage systems described above may also implement AI platforms
for delivering on the vision of self-driving storage. Such AI
platforms may be configured to deliver global predictive
intelligence by collecting and analyzing large amounts of storage
system telemetry data points to enable effortless management,
analytics and support. In fact, such storage systems may be capable
of predicting both capacity and performance, as well as generating
intelligent advice on workload deployment, interaction and
optimization. Such AI platforms may be configured to scan all
incoming storage system telemetry data against a library of issue
fingerprints to predict and resolve incidents in real-time, before
they impact customer environments, and captures hundreds of
variables related to performance that are used to forecast
performance load.
The storage systems described above may support the serialized or
simultaneous execution of artificial intelligence applications,
machine learning applications, data analytics applications, data
transformations, and other tasks that collectively may form an AI
ladder. Such an AI ladder may effectively be formed by combining
such elements to form a complete data science pipeline, where exist
dependencies between elements of the AI ladder. For example, AI may
require that some form of machine learning has taken place, machine
learning may require that some form of analytics has taken place,
analytics may require that some form of data and information
architecting has taken place, and so on. As such, each element may
be viewed as a rung in an AI ladder that collectively can form a
complete and sophisticated AI solution.
The storage systems described above may also, either alone or in
combination with other computing environments, be used to deliver
an AI everywhere experience where AI permeates wide and expansive
aspects of business and life. For example, AI may play an important
role in the delivery of deep learning solutions, deep reinforcement
learning solutions, artificial general intelligence solutions,
autonomous vehicles, cognitive computing solutions, commercial UAVs
or drones, conversational user interfaces, enterprise taxonomies,
ontology management solutions, machine learning solutions, smart
dust, smart robots, smart workplaces, and many others. The storage
systems described above may also, either alone or in combination
with other computing environments, be used to deliver a wide range
of transparently immersive experiences where technology can
introduce transparency between people, businesses, and things. Such
transparently immersive experiences may be delivered as augmented
reality technologies, connected homes, virtual reality
technologies, brain-computer interfaces, human augmentation
technologies, nanotube electronics, volumetric displays, 4D
printing technologies, or others. The storage systems described
above may also, either alone or in combination with other computing
environments, be used to support a wide variety of digital
platforms. Such digital platforms can include, for example, 5G
wireless systems and platforms, digital twin platforms, edge
computing platforms, IoT platforms, quantum computing platforms,
serverless PaaS, software-defined security, neuromorphic computing
platforms, and so on.
Readers will appreciate that some transparently immersive
experiences may involve the use of digital twins of various
"things" such as people, places, processes, systems, and so on.
Such digital twins and other immersive technologies can alter the
way that humans interact with technology, as conversational
platforms, augmented reality, virtual reality and mixed reality
provide a more natural and immersive interaction with the digital
world. In fact, digital twins may be linked with the real-world,
perhaps even in real-time, to understand the state of a thing or
system, respond to changes, and so on. Because digital twins
consolidate massive amounts of information on individual assets and
groups of assets (even possibly providing control of those assets),
digital twins may communicate with each other to digital factory
models of multiple linked digital twins.
The storage systems described above may also be part of a
multi-cloud environment in which multiple cloud computing and
storage services are deployed in a single heterogeneous
architecture. In order to facilitate the operation of such a
multi-cloud environment, DevOps tools may be deployed to enable
orchestration across clouds. Likewise, continuous development and
continuous integration tools may be deployed to standardize
processes around continuous integration and delivery, new feature
rollout and provisioning cloud workloads. By standardizing these
processes, a multi-cloud strategy may be implemented that enables
the utilization of the best provider for each workload.
Furthermore, application monitoring and visibility tools may be
deployed to move application workloads around different clouds,
identify performance issues, and perform other tasks. In addition,
security and compliance tools may be deployed for to ensure
compliance with security requirements, government regulations, and
so on. Such a multi-cloud environment may also include tools for
application delivery and smart workload management to ensure
efficient application delivery and help direct workloads across the
distributed and heterogeneous infrastructure, as well as tools that
ease the deployment and maintenance of packaged and custom
applications in the cloud and enable portability amongst clouds.
The multi-cloud environment may similarly include tools for data
portability.
The storage systems described above may be used as a part of a
platform to enable the use of crypto-anchors that may be used to
authenticate a product's origins and contents to ensure that it
matches a blockchain record associated with the product. Such
crypto-anchors may take many forms including, for example, as
edible ink, as a mobile sensor, as a microchip, and others.
Similarly, as part of a suite of tools to secure data stored on the
storage system, the storage systems described above may implement
various encryption technologies and schemes, including lattice
cryptography. Lattice cryptography can involve constructions of
cryptographic primitives that involve lattices, either in the
construction itself or in the security proof. Unlike public-key
schemes such as the RSA, Diffie-Hellman or Elliptic-Curve
cryptosystems, which are easily attacked by a quantum computer,
some lattice-based constructions appear to be resistant to attack
by both classical and quantum computers.
A quantum computer is a device that performs quantum computing.
Quantum computing is computing using quantum-mechanical phenomena,
such as superposition and entanglement. Quantum computers differ
from traditional computers that are based on transistors, as such
traditional computers require that data be encoded into binary
digits (bits), each of which is always in one of two definite
states (0 or 1). In contrast to traditional computers, quantum
computers use quantum bits, which can be in superpositions of
states. A quantum computer maintains a sequence of qubits, where a
single qubit can represent a one, a zero, or any quantum
superposition of those two qubit states. A pair of qubits can be in
any quantum superposition of 4 states, and three qubits in any
superposition of 8 states. A quantum computer with n qubits can
generally be in an arbitrary superposition of up to 2{circumflex
over ( )}n different states simultaneously, whereas a traditional
computer can only be in one of these states at any one time. A
quantum Turing machine is a theoretical model of such a
computer.
The storage systems described above may also be paired with
FPGA-accelerated servers as part of a larger AI or ML
infrastructure. Such FPGA-accelerated servers may reside near
(e.g., in the same data center) the storage systems described above
or even incorporated into an appliance that includes one or more
storage systems, one or more FPGA-accelerated servers, networking
infrastructure that supports communications between the one or more
storage systems and the one or more FPGA-accelerated servers, as
well as other hardware and software components. Alternatively,
FPGA-accelerated servers may reside within a cloud computing
environment that may be used to perform compute-related tasks for
AI and ML jobs. Any of the embodiments described above may be used
to collectively serve as a FPGA-based AI or ML platform. Readers
will appreciate that, in some embodiments of the FPGA-based AI or
ML platform, the FPGAs that are contained within the
FPGA-accelerated servers may be reconfigured for different types of
ML models (e.g., LSTMs, CNNs, GRUs). The ability to reconfigure the
FPGAs that are contained within the FPGA-accelerated servers may
enable the acceleration of a ML or AI application based on the most
optimal numerical precision and memory model being used. Readers
will appreciate that by treating the collection of FPGA-accelerated
servers as a pool of FPGAs, any CPU in the data center may utilize
the pool of FPGAs as a shared hardware microservice, rather than
limiting a server to dedicated accelerators plugged into it.
The FPGA-accelerated servers and the GPU-accelerated servers
described above may implement a model of computing where, rather
than keeping a small amount of data in a CPU and running a long
stream of instructions over it as occurred in more traditional
computing models, the machine learning model and parameters are
pinned into the high-bandwidth on-chip memory with lots of data
streaming though the high-bandwidth on-chip memory. FPGAs may even
be more efficient than GPUs for this computing model, as the FPGAs
can be programmed with only the instructions needed to run this
kind of computing model.
The storage systems described above may be configured to provide
parallel storage, for example, through the use of a parallel file
system such as BeeGFS. Such parallel files systems may include a
distributed metadata architecture. For example, the parallel file
system may include a plurality of metadata servers across which
metadata is distributed, as well as components that include
services for clients and storage servers. Through the use of a
parallel file system, file contents may be distributed over a
plurality of storage servers using striping and metadata may be
distributed over a plurality of metadata servers on a directory
level, with each server storing a part of the complete file system
tree. Readers will appreciate that in some embodiments, the storage
servers and metadata servers may run in userspace on top of an
existing local file system. Furthermore, dedicated hardware is not
required for client services, the metadata servers, or the hardware
servers as metadata servers, storage servers, and even the client
services may be run on the same machines.
The systems described above can support the execution of a wide
array of software applications. Such software applications can be
deployed in a variety of ways, including container-based deployment
models. Containerized applications may be managed using a variety
of tools. For example, containerized applications may be managed
using Docker Swarm, a clustering and scheduling tool for Docker
containers that enables IT administrators and developers to
establish and manage a cluster of Docker nodes as a single virtual
system. Likewise, containerized applications may be managed through
the use of Kubernetes, a container-orchestration system for
automating deployment, scaling and management of containerized
applications. Kubernetes may execute on top of operating systems
such as, for example, Red Hat Enterprise Linux, Ubuntu Server, SUSE
Linux Enterprise Servers, and others. In such examples, a master
node may assign tasks to worker/minion nodes. Kubernetes can
include a set of components (e.g., kubelet, kube-proxy, cAdvisor)
that manage individual nodes as a well as a set of components
(e.g., etcd, API server, Scheduler, Control Manager) that form a
control plane. Various controllers (e.g., Replication Controller,
DaemonSet Controller) can drive the state of a Kubernetes cluster
by managing a set of pods that includes one or more containers that
are deployed on a single node. Containerized applications may be
used to facilitate a serverless, cloud native computing deployment
and management model for software applications. In support of a
serverless, cloud native computing deployment and management model
for software applications, containers may be used as part of an
event handling mechanisms (e.g., AWS Lambdas) such that various
events cause a containerized application to be spun up to operate
as an event handler.
The systems described above may be deployed in a variety of ways,
including being deployed in ways that support fifth generation
(`5G`) networks. 5G networks may support substantially faster data
communications than previous generations of mobile communications
networks and, as a consequence may lead to the disaggregation of
data and computing resources as modern massive data centers may
become less prominent and may be replaced, for example, by
more-local, micro data centers that are close to the mobile-network
towers. The systems described above may be included in such local,
micro data centers and may be part of or paired to multi-access
edge computing (`MEC`) systems. Such MEC systems may enable cloud
computing capabilities and an IT service environment at the edge of
the cellular network. By running applications and performing
related processing tasks closer to the cellular customer, network
congestion may be reduced and applications may perform better. MEC
technology is designed to be implemented at the cellular base
stations or other edge nodes, and enables flexible and rapid
deployment of new applications and services for customers. MEC may
also allow cellular operators to open their radio access network
(`RAN`) to authorized third-parties, such as application developers
and content provider. Furthermore, edge computing and micro data
centers may substantially reduce the cost of smartphones that work
with the 5G network because customer may not need devices with such
intensive processing power and the expensive requisite
components.
Readers will appreciate that 5G networks may generate more data
than previous network generations, especially in view of the fact
that the high network bandwidth offered by 5G networks may cause
the 5G networks to handle amounts and types of data (e.g., sensor
data from self-driving cars, data generated by AR/VR technologies)
that weren't as feasible for previous generation networks. In such
examples, the scalability offered by the systems described above
may be very valuable as the amount of data increases, adoption of
emerging technologies increase, and so on.
For further explanation, FIG. 3D illustrates an exemplary computing
device 350 that may be specifically configured to perform one or
more of the processes described herein. As shown in FIG. 3D,
computing device 350 may include a communication interface 352, a
processor 354, a storage device 356, and an input/output ("I/O")
module 358 communicatively connected one to another via a
communication infrastructure 360. While an exemplary computing
device 350 is shown in FIG. 3D, the components illustrated in FIG.
3D are not intended to be limiting. Additional or alternative
components may be used in other embodiments. Components of
computing device 350 shown in FIG. 3D will now be described in
additional detail.
Communication interface 352 may be configured to communicate with
one or more computing devices. Examples of communication interface
352 include, without limitation, a wired network interface (such as
a network interface card), a wireless network interface (such as a
wireless network interface card), a modem, an audio/video
connection, and any other suitable interface.
Processor 354 generally represents any type or form of processing
unit capable of processing data and/or interpreting, executing,
and/or directing execution of one or more of the instructions,
processes, and/or operations described herein. Processor 354 may
perform operations by executing computer-executable instructions
362 (e.g., an application, software, code, and/or other executable
data instance) stored in storage device 356.
Storage device 356 may include one or more data storage media,
devices, or configurations and may employ any type, form, and
combination of data storage media and/or device. For example,
storage device 356 may include, but is not limited to, any
combination of the non-volatile media and/or volatile media
described herein. Electronic data, including data described herein,
may be temporarily and/or permanently stored in storage device 356.
For example, data representative of computer-executable
instructions 362 configured to direct processor 354 to perform any
of the operations described herein may be stored within storage
device 356. In some examples, data may be arranged in one or more
databases residing within storage device 356.
I/O module 358 may include one or more I/O modules configured to
receive user input and provide user output. I/O module 358 may
include any hardware, firmware, software, or combination thereof
supportive of input and output capabilities. For example, I/O
module 358 may include hardware and/or software for capturing user
input, including, but not limited to, a keyboard or keypad, a
touchscreen component (e.g., touchscreen display), a receiver
(e.g., an RF or infrared receiver), motion sensors, and/or one or
more input buttons.
I/O module 358 may include one or more devices for presenting
output to a user, including, but not limited to, a graphics engine,
a display (e.g., a display screen), one or more output drivers
(e.g., display drivers), one or more audio speakers, and one or
more audio drivers. In certain embodiments, I/O module 358 is
configured to provide graphical data to a display for presentation
to a user. The graphical data may be representative of one or more
graphical user interfaces and/or any other graphical content as may
serve a particular implementation. In some examples, any of the
systems, computing devices, and/or other components described
herein may be implemented by computing device 350.
Advantages and features of the present disclosure can be further
described by the following statements: 1. A method of receiving, by
a processing device from a first solid-state storage device of a
plurality of solid-state storage devices, an indication that data
stored at the first solid-state storage device is affected by read
disturb; in response to receiving the indication, identifying a
second solid-state storage device of the plurality of solid-state
storage devices for the relocation of the data; and transmitting,
to the first solid-state storage device, a command comprising
information associated with the relocation of the data from the
first solid-state storage device to the second solid-state storage
device, wherein the command causes the first solid-state storage
device to relocate the data to the second solid-state storage
device. 2. The method of statement 1, wherein the information
associated with the relocation of the data comprises one or more
transformation operations to be performed on the data and wherein
the one or more transformation operations comprise at least one of
de-encryption of the data, encryption of the data, a format change
of the data or a modification to encoded metadata associated with
the data. 3. The method of statement 1 or statement 2, wherein the
data is transmitted from the first solid-state storage device to
the second solid-state storage device via a mesh network or a
network switch. 4. The method of statement 1, statement 2 or
statement 3, further comprising receiving, from the second
solid-state storage device, an acknowledgment that the data has
been relocated; and in response to receiving the acknowledgment,
updating a mapping data structure to deallocate the data stored at
the first solid-state storage device and associate the data with
the second solid-state storage device.
5. The method of statement 1, statement 2, statement 3 or statement
4, wherein the command comprising the information associated with
the relocation of the data comprises an identification of the data
stored in an encrypted segment of data, wherein the command causes
the first solid-state storage device to decrypt the encrypted
segment and extract the data from the encrypted segment. 6. The
method of statement 1, statement 2, statement 3, statement 4 or
statement 5, wherein the relocation of the data from the first
solid-state storage device to the second solid-state storage device
is associated with an accelerated garbage collection operation. 7.
The method of statement 1, statement 2, statement 3, statement 4,
statement 5 or statement 6, wherein the data being affected by read
disturb comprises at least one of a read count associated with the
data satisfying a read count threshold; an error rate associated
with the data satisfying an error rate threshold; or an amount of
time associated with the data satisfying a time threshold.
FIG. 4 is an example method 400 to locally program data stored at
storage device to a new data block in accordance with embodiments
of the disclosure. In general, the method 400 may be performed by
processing logic that may include hardware (e.g., processing
device, circuitry, dedicated logic, programmable logic, microcode,
hardware of a device, integrated circuit, etc.), software (e.g.,
instructions run or executed on a processing device), or a
combination thereof. In some embodiments, processing logic of a
storage controller of a storage system, as previously described at
FIGS. 1A-3D, may perform the method 400.
Method 400 may begin at block 402, where the processing logic
receives, from a storage device, an indication of an occurrence of
a triggering event associated with data stored at a first data
block of the storage device. The storage device may track and
monitor information associated with the data stored at the storage
device. For example, the storage device may track the number of
read operations performed on the data, an amount of time that has
elapsed since the data was programmed to the storage device, an
error rate associated with the data, etc. The storage device may
also include one or more thresholds associated with the tracked
information. For example, the storage device may include a read
count threshold, time threshold, and/or an error rate threshold. As
another example, characteristics such as number of retries, number
of corrected errors, increase in correctable errors over a period
time or since the last read of the same blocks, detection of an
increase in correctable errors after recent nearby reads, voltage
level adjustments needed to read data from Flash memory cells, or
additional data that the storage device, Flash memory chips,
alternate solid state memory devices, or Flash memory or solid
state memory controllers can generate, can be fed into a set of
heuristics that decide whether action is required. A heuristic
might instead generate a change in the level of confidence in a
data block which the storage system logic running in a storage
controller might use to prioritize whether or when to perform
cautionary preventative actions.
In embodiments, a triggering event associated with the data may
occur when one or more of the thresholds associated with the
tracked information are satisfied. For example, if a read count
associated with the data satisfies a read count threshold, then a
triggering event associated with the data has occurred. In some
embodiments, a threshold may be satisfied if the tracked
information is greater than or equal to the threshold. In other
embodiments, a threshold may be satisfied if the tracked
information is less than or equal to the threshold. In embodiments,
multiple thresholds may be used.
Upon determining that a triggering event has occurred, the storage
device may transmit the indication of the occurrence of the
triggering event to the storage controller. The indication may
include address information corresponding to the first data block
storing the data. The indication may also include information
associated with the triggering event. For example, the indication
may include a read count associated with the data stored at data
block A and may also indicate that the read count satisfied the
read count threshold.
In some embodiments, upon receiving the indication, the processing
logic may perform an analysis of one or more regions of the storage
device to determine whether the regions have been affected by read
disturb. For example, the processing logic may query the storage
device for information to determine regions affected by read
disturb. Examples of information used to determine regions affected
by read disturb may include, but are not limited to, read counts,
number of times data blocks of a region have been reprogrammed,
whether any level adjustments were made to read data from the
regions or if any difficulties in reading the data stored in a
region occurred.
At block 404, the processing logic identifies a second data block
of the storage device for storage of the data. Upon receiving the
indication at block 402, the processing logic may identify a second
data block of the storage device that is available for storing data
and allocate the second data block of the storage device that the
data is to be programmed (e.g., relocated) to. For example, the
processing logic may identify and allocate data block B of the
storage device for storing the data.
At block 406, the processing logic transmits, to the storage
device, a command that includes address information of the second
data block of the storage device. The address information may
correspond to the address of the second data block on the storage
device. In embodiments, the command may also include address
information of the first data block currently storing the data to
enable the storage device to locate the data associated with the
triggering event. For example, the command may include address
information associated with data block A that is currently storing
the data and address information associated with data block B that
is to store the relocated data. The command causes the storage
device to program the data stored at the first data block of the
storage device to the second data block of the storage device. For
example, in response to receiving the command, the storage device
may program a copy of the data stored at data block A to data block
B. Because the data stored at the second data block has been
recently programmed, errors that may be present in the data stored
at the first data block (e.g., the original copy of the data) due
to retention time, read disturb, etc. may be mitigated. In
embodiments, the command may also cause the storage device to
program metadata associated with the data, such as metadata
identifying a type or classification of the data, to the second
data block.
In some embodiments, the command may correspond to a logical
description of multiple changes that are transmitted to the storage
device as a composite atomic operation. In a composite atomic
operation, the storage device ensures that the elements (e.g.,
changes) of the composite atomic operation either complete entirely
or none of the elements of the composite atomic operation complete.
The storage device may not transmit an acknowledgement of
completion of the composite atomic operation to the processing
logic until each of the elements of the composite atomic operation
have completed entirely.
At block 408, the processing logic receives, from the storage
device, an acknowledgement that the data has been programmed to the
second data block. For example, upon completion of programming a
copy of the data to the second data block, the storage device may
transmit an acknowledgment to the processing logic to notify the
processing logic that the data at the second data block has been
programmed and is accessible.
Upon receiving the acknowledgment that the data at the second data
block is accessible, at block 410, the processing logic updates a
mapping data structure for the first data block and the second data
block. The mapping data structure may correlate a logical address
associated with the data stored at the storage device with an
address of the data block storing the data at the storage device.
Once the data is accessible at the second data block, the
processing logic may update the mapping data structure to associate
the logical address of the data with the address of the second data
block. The processing logic may then update the mapping data
structure to deallocate the first data block. For example, the
processing logic may update the mapping data structure to
deallocate data block A and associate the data with data block
B.
FIG. 5 is an example method 500 to locally relocate data stored at
storage device to a new data block in accordance with embodiments
of the disclosure. In general, the method 500 may be performed by
processing logic that may include hardware (e.g., processing
device, circuitry, dedicated logic, programmable logic, microcode,
hardware of a device, integrated circuit, etc.), software (e.g.,
instructions run or executed on a processing device), or a
combination thereof. In some embodiments, processing logic of a
storage controller of a storage system, as previously described at
FIGS. 1A-3D, may perform the method 500.
Method 500 may begin at block 502, where the processing logic
receives, from a storage device, an indication that data stored at
a first data block of the storage device is to be relocated. In
embodiments, the data may be identified by the storage device for
relocation based on the occurrence of a triggering event associated
with the data, as previously described.
At block 504, the processing logic determines whether to relocate
data from the first data block to a second data block of the
storage device. For example, if the processing logic determines
that the data stored at the storage device is no longer active or
has been de-allocated, then the processing logic may determine to
not have the storage device locally relocate the data to a new
location on the storage device.
In some embodiments, the processing logic may determine to relocate
the data to a different storage device of the storage system. For
example, the processing logic may determine that the storage device
storing the data has been marked for removal or for accelerated
garbage collection as a preventative action or that all data stored
at the storage device is to be evacuated. Accordingly, the
processing logic may determine to relocate the data to a different
storage device that has not been marked for removal/evacuation. In
another example, the processing logic may determine to relocate the
data to a different storage device based on wear leveling or load
balancing of the storage devices of the storage system. For
example, if the storage device currently has a higher number of
program/erase (P/E) cycles or less storage capacity available than
another storage device of the storage system, then the processing
logic may determine to relocate the data to the other storage
device of the storage system.
In some embodiments, the storage controller may determine to
initiate an accelerated garbage collection operation. The
accelerated garbage collection may result in a command to perform a
partial transfer of some data to be combined with other data stored
at the storage system. In embodiments, the data to be relocated may
be part of shard of an erasure coded protected stripe that includes
shards stored across several storage devices of the storage system.
The relocation of the data in the erasure coded protected stripe
may trigger the recalculation of erasure coded protection
information for the erasure coded stripe.
If the processing logic determines to relocate the data from the
first data block to the second data block, at block 506, the
processing logic transmits, to the storage device, a command
including address information of the second data block of the
storage device. The command may cause the storage device to
relocate the data from the first data block to the second data
block, as previously described.
At block 508, the processing logic receives, from the storage
device, an acknowledgment that the data has been relocated to the
second data block, as previously described.
At block 510, the processing logic updates a mapping data structure
for the first data block and the second data block, as previously
described.
FIG. 6A is an illustration of an example of a storage device of a
storage system 600 tracking information associated with data stored
at the storage device in accordance with embodiments of the
disclosure. The storage system 600 includes a storage controller
604 operatively coupled to a storage device 602 of storage system
600. The storage device 602 may include a data block 606 that
stores data 608. It should be noted that data 608 is shown for
illustrative purposes and is not a physical component of storage
device 602.
Storage device 602 may track the read count 616, the error rate 618
and the time 620 associated with data 608. The read count 616 may
correspond to the number of read operations performed on data 608.
The error rate 618 may be a bit error rate (BER) that corresponds
to a number of bit errors in a sequence of bits for data 608. For
example, a sequence of 10 bits of data that contains 3 bit errors
may have a BER of 0.3 or 30%. The time 620 may correspond to the
amount of time that has elapsed since data 608 was programmed to
storage device 602.
The storage device 602 may include a read threshold 610, an error
threshold 612 and a time threshold 614. The read threshold 610 may
correspond to a number of read operations performed on data, such
as data 608, stored at storage device 602. In embodiments, the read
threshold 610 may be determined based on a number of read
operations that can be performed on data 608 before data stored at
storage device 602 is at risk of being affected by read disturb
stress. The error threshold 612 may correspond to an error rate for
data stored at the storage device 602. In embodiments, the error
threshold 612 may be determined based on a correction capability of
error-correction code (ECC) of the storage device 602. For example,
the error threshold 612 may be a value that is less than the
correction capability of the ECC to ensure data stored at storage
device 602 can be corrected by the ECC. The time threshold 614 may
correspond to an amount of time that has elapsed since data was
programmed to storage device 602. In embodiments, the time
threshold 614 may be determined based on the data retention time
for data stored at storage device 602. For example, if data can no
longer reliably be stored at storage device 602 after 30 days have
elapsed since the data was programmed, then the time threshold 614
value may be determined to be 30 days or less.
The storage device 602 may compare the read count 616, the error
rate 618 and the time 620 associated with data 608 to the read
threshold 610, the error threshold 612 and the time threshold 614,
respectively. A triggering event occurs when any of the read count
616, error rate 618 or time 620 satisfies their respective
thresholds. The occurrence of the triggering event may cause
storage device 602 to transmit an indication to storage controller
604, as previously described.
Referring to FIG. 6A, read threshold 610 is satisfied if the read
count 616 is greater than or equal to the read threshold 610, error
threshold 612 is satisfied if the error rate 618 is greater than or
equal to the error threshold 612 and time threshold 614 is
satisfied if time 620 is greater than or equal to the time
threshold 614. In FIG. 6A, because read count 616, error rate 618
and time 620 do not satisfy their respective thresholds, a
triggering event has not occurred. Accordingly, storage device 602
determines to not send an indication to storage controller 604.
FIG. 6B is an illustration of an example of a storage device of a
storage system 625 transmitting an indication to a storage
controller in accordance with embodiments of the disclosure.
Referring to FIG. 6B, the read count 616 associated with data 608
is equal to the read threshold 610. Therefore, the read count 616
associated with the data 608 satisfies the read threshold 610 and a
triggering event has occurred. In response to determining that a
triggering event has occurred, storage device 602 transmits an
indication 627 to storage controller 604 that indicates a
triggering event associated with data 608 has occurred. The
indication 627 may further include information associated with the
triggering event, as previously described.
FIG. 6C is an illustration of an example of a storage device of a
storage system 650 relocating data from a first data block to a
second data block in accordance with embodiments of the disclosure.
Referring to FIG. 6C, in response to receiving the indication as
described in FIG. 6B, the storage controller 604 may transmit a
command 652 to the storage device 602. The command 652 may include
address information associated with a new data block (e.g., data
block 654) of storage device 602 for storage of data 608a. Upon
receiving the command 652, storage device 602 may relocate data
608a by programming a copy of data 608a (e.g., data 608b) to data
block 654.
In some embodiments, upon programming data 608b to data block 654,
the storage device 602 may transmit an acknowledgment to storage
controller 604 indicating that the storage device 602 has completed
the relocation of data 608. Upon receipt of the acknowledgment, the
storage controller 604 may update a mapping data structure for data
block 606 and data block 654, as previously described.
FIG. 7A is an illustration of an example of a storage device of a
storage system 700 storing data at a host system buffer in
accordance with embodiments of the disclosure. In some embodiments,
when relocating data 706 from data block 704 to data block 708,
storage device 702 may store data 706 at a host system buffer 714.
In embodiments, storage system 700 may include a shelf controller
710 that manages one or more storage devices (e.g., storage device
702) of a shelf of storage system 700. The shelf controller 710 may
include a processing device 712 that is operatively coupled to a
host system buffer 714. The host system buffer 714 may be a local
memory, such as NVRAM, of shelf controller 710.
Referring to 7A, storage device 702 has received a command to
relocate data 706 from a storage controller (not shown) of storage
system 700, as previously described. In response to receiving the
command, storage device 702 may begin the relocation process for
data 706. The storage device 702 may have permissions from shelf
controller 710 to temporarily store a copy of data 706a (e.g., data
706b) at host system buffer 714. The storage device 702 may
transmit the copy of data 706a to the shelf controller 710, where
data 706b is received and stored at the host system buffer 714 by
processing device 712. In some embodiments, upon storing data 706b
at the host system buffer 714, one or more transformative
operations may be performed on data 706b to prepare data 706b for
storage at data block 708 of the storage device 702.
FIG. 7B is an illustration of an example of a storage device of a
storage system 750 receiving data from a host system buffer for
storage at a new data block of the storage device in accordance
with embodiments of the disclosure. In embodiments, after
temporarily storing data 706b at the host system buffer 714,
storage device 702 may transmit a request for data 706b to the
shelf controller 710. The shelf controller 710 may then transmit a
copy of data 706b (e.g., data 706c) to the storage device 702 via
processing device 712. Upon receipt of data 706c, the storage
device 702 may program data 706c to data block 708.
In some embodiments, upon programming data 706c to data block 708,
the storage device 702 may transmit an acknowledgment to a storage
controller (not shown) of storage system 750 indicating that the
storage device 702 has completed the relocation of data 706. Upon
receipt of the acknowledgment, the storage controller may update a
mapping data structure for data block 704 and data block 654, as
previously described.
FIG. 8A is an illustration of an example of a storage controller of
a storage system 800 transmitting a command to a first storage
device to relocate data to a second storage device in accordance
with embodiments of the disclosure. The storage system 800 includes
a storage controller 804 operatively coupled to storage devices 802
and 812 of storage system 800. Storage device 802 may include a
data block 806 that stores data 808. It should be noted that data
808 is shown for illustrative purposes and is not a physical
component of storage device 802. Storage device 812 may include a
data block 814 for storage of data.
As previously described, in some embodiments rather than a storage
device (e.g., storage device 802) locally relocating the data to a
different data block, the data may be relocated to a different
storage device of the storage system. The storage controller 804
may transmit a command 810 to the storage device 802 to cause the
relocation of data 808 from storage device 802 to storage device
812. The command 810 may include address information associated
with data block 806 storing data 808. The command 810 may include
address information associated with data block 814 of storage
device 812 where data 808 is to be relocated to.
FIG. 8B is an illustration of a first storage device of a storage
system 825 relocating data to a second storage device of the
storage system in accordance with an embodiment of the disclosure.
Upon receiving the command 810, storage device 802 may relocate
data 808a by transmitting a copy of data 808a (e.g., data 808b) to
storage device 812. In embodiments, the copy of data 808a may be
transmitted from storage device 802 to storage device 812 via a
mesh network or a network switch. In embodiments, storage device
802 may also transmit address information associated with data
block 814 to storage device 812 to indicate that the copy of data
808a is to be stored at data block 814. Upon receiving the copy of
data 808a, storage device 812 may program data 808b to data block
814.
FIG. 8C is an illustration of a second storage device of storage
system 850 transmitting an acknowledgement to a storage controller
in accordance with some embodiments of the disclosure. Upon
programming data 808b to data block 814, the storage device 812 may
transmit an acknowledgment to storage controller 804 indicating
that the storage device 812 has completed the programming of data
808b. Upon receipt of the acknowledgment, the storage controller
804 may update a mapping data structure for data block 806 of
storage device 802 and data block 814 of storage device 812, as
previously described.
FIG. 9 is an illustration of an example of a storage controller of
storage system 900 transmitting a command to a storage device to
extract data from an encrypted segment in accordance with
embodiments of the disclosure. The storage system 900 includes a
storage controller 904 operatively coupled to a storage device 902
storage system 900. Storage device 902 may include an encrypted
segment 906 of data stored at the storage device 902. For example,
data may be encrypted and programmed to storage device 902 in 1 MB
segments for storage. The encrypted segment 906 may include data
908 to be relocated as well as other data 914 stored at the storage
device 902. It should be noted that encrypted segment 906, data 908
and other data 914 are shown for illustrative purposes and are not
physical components of storage device 902.
To access the data 908 stored in the encrypted segment 906, the
storage controller 904 may transmit a command 910 to the storage
device 902 to relocate data 908. The command 910 may include
address information associated with the encrypted segment 906 as
well as address information of data 908. The command 910 may cause
the storage device 902 to decrypt the encrypted segment 906 to
access data 908 for relocation. The decrypted segment 912 may
include data 908 in a decrypted form and other data 914 in a
decrypted form. In some embodiments, the storage device 902 may
relocate data 908 to a new data block of storage device 902. In
other embodiments, the storage device 902 may relocate data 908 to
a different storage device (not shown) of storage system 900, as
previously described.
FIG. 10 is an illustration of an example of performing a
transformation operation on data being relocated by a storage
system 1000 in accordance with embodiments of the disclosure. The
storage system 1000 includes a storage controller 1004 operatively
coupled to storage devices 1002 and 1012 of storage system 1000.
Storage device 1002 may include a data block 1006 that stores data
1008.
Referring to FIG. 10, storage controller 1004 may transmit a
command 1020 to storage device 1002 to relocate data 1008 to
storage device 1012, as previously described. However, the command
1020 may also include one or more transformation operations to be
performed on the data 1008 during the relocation process. In
embodiments, the transformation operation may be the encryption
and/or decryption of data 1008. For example, data 1008 may be
encrypted during relocation to storage device 1012. In some
embodiments, the transformation operation may be changing the
format or structure of data 1008. In an embodiment, the
transformation operation may be a compression of data 1008. In an
embodiment, the transformation operation may be a modification to
encoded metadata to handle data being relocated to a new location,
which may result in the data having different relationships to
previous erasure coded stripes. In embodiments, the transformation
operation may be any type of operation that transforms data
1008.
In an example scenario, if the data that is transformed and
relocated is part of an erasure coded set, then parity blocks
associated with the set become incorrect and may need to be
rewritten. In some embodiments, the alterations/transformations may
be monitored and a map may be generated. The map may correspond to
a map of bytes that correspond to bytes of the data that have
changed as a result of the relocation and transformation. The map
of the bytes that have changed as a result of the relocation and
transformation and the initial bytes (bytes of the data before
relocation and transformation) may be provided to the storage
devices that store the parity blocks. The storage devices may then
receive a command to write new parity blocks by subtracting the
initial bytes of data from a data stripe being moved and then
adding in the bytes changed by the relocation and transformation of
the data.
Upon receiving command 1020 from storage controller 1004, the
storage device 1002 may begin the relocation of data 1008. During
the relocation of data 1008, the transformation operation(s)
indicated in command 1020 are performed on data 1008 to generate
transformed data 1016 (block 1010). In some embodiments, the
transformation operation(s) may be performed by storage device
1002. For example, storage device 1002 may encrypt data 1008 to
generate transformed data 1016 (e.g., data 1008 in encrypted form)
and transmit transformed data to storage device 1012 for storage.
In embodiments, the transformation operation(s) may be performed by
storage device 1012. For example, upon receiving a copy of data
1008 from storage device 1002, storage device 1012 may change the
format of the copy of data 1008 to generate transformed data 1016
(e.g., the copy of data 1008 with the new format). In an
embodiment, a first portion of the transformation operation(s) may
be performed by storage device 1002 and a second portion of the
transformation operation(s) may be performed by storage device
1012. In some embodiments, data 1008 may include parity blocks that
may be relocated and transformation operation(s) may be performed
on associated metadata that is not party of the parity.
Once the transformation operation(s) have been performed on a copy
of data 1008 to produce transformed data 1016, storage device 1012
may program transformed data 1016 to data block 1014 for storage.
Upon programming the transformed data 1016, storage device 1012 may
transmit an acknowledgement 1018 to storage controller 1004, as
previously described. Although FIG. 10 described transforming data
being relocated from a first storage device to a second storage
device of storage system 1000, aspects of FIG. 10 may be utilized
to transform data being locally relocated by a storage device to a
new data block of the storage device.
FIG. 11 is an example method 1100 to relocate data stored at
storage device to a different storage device in accordance with
embodiments of the disclosure. In general, the method 1100 may be
performed by processing logic that may include hardware (e.g.,
processing device, circuitry, dedicated logic, programmable logic,
microcode, hardware of a device, integrated circuit, etc.),
software (e.g., instructions run or executed on a processing
device), or a combination thereof. In some embodiments, processing
logic of a storage controller of a storage system, as previously
described at FIGS. 1A-3D, may perform the method 1100.
Method 1100 may begin at block 1102, where the processing logic
receives, from a first storage device, an indication that data
stored at the first storage device is affected by read disturb. The
first storage device may determine the data is affected by read
disturb using an associated error rate, read count, amount of time,
number of read retries or any other type of heuristic as previously
described.
At block 1104, the processing logic identifies a second storage
device for relocation of the data that has been affected by read
disturb.
At block 1106, the processing logic transmits, to the first storage
device, a command including information associated with the
relocation of the data from the first storage device to the second
storage device.
At block 1108, the processing logic receives, from the second
storage device, an acknowledgment that the data has been
relocated.
At block 1110, the processing logic updates a mapping data
structure for the first storage device and the second storage
device.
While this specification contains many specific implementation
details, these should not be construed as limitations on the scope
of any inventions or of what may be claimed, but rather as
descriptions of features specific to particular embodiments of
particular inventions. Certain features that are described in this
specification in the context of separate embodiments can also be
implemented in combination in a single embodiment. Conversely,
various features that are described in the context of a single
embodiment can also be implemented in multiple embodiments
separately or in any suitable subcombination. Moreover, although
features may be described above as acting in certain combinations
and even initially claimed as such, one or more features from a
claimed combination can in some cases be excised from the
combination, and the claimed combination may be directed to a
subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a
particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. In certain circumstances,
multitasking and parallel processing may be advantageous. Moreover,
the separation of various system components in the embodiments
described above should not be understood as requiring such
separation in all embodiments, and it should be understood that the
described program components and systems can generally be
integrated together in a single software product or packaged into
multiple software products.
Thus, particular embodiments of the subject matter have been
described. Other embodiments are within the scope of the following
claims. In some cases, the actions recited in the claims can be
performed in a different order and still achieve desirable results.
In addition, the processes depicted in the accompanying figures do
not necessarily require the particular order shown, or sequential
order, to achieve desirable results. In certain implementations,
multitasking and parallel processing may be advantageous.
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